{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# 数据预处理常用库\n",
    "from sklearn import preprocessing\n",
    "import time\n",
    "from datetime import datetime\n",
    "# Scipy是一个用于数学、科学、工程领域的常用软件包，可以处理插值、积分、优化、图像处理、常微分方程数值解的求解、信号处理等问题\n",
    "from scipy import integrate, optimize\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# LightGBM是个快速的，分布式的，高性能的基于决策树算法的梯度提升框架。可用于排序，分类，回归以及很多其他的机器学习任务中\n",
    "import lightgbm as lgb\n",
    "# XGBoost是一种提升树模型，它是将许多树模型集成在一起，形成一个很强的分类器，所用到的树模型则是CART回归树模型。\n",
    "import xgboost as xgb\n",
    "from xgboost import plot_importance, plot_tree\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "#绘制图像的背景长高\n",
    "rcParams['figure.figsize'] = 50,22\n",
    "#用来正常显示中文标签\n",
    "rcParams['font.sans-serif']=['SimHei']\n",
    "#用来正常显示负号\n",
    "rcParams['axes.unicode_minus'] = False\n",
    "#所有警告只出现一次，避免警告占用太多输出页面，在进行循环时这项设置尤其有用\n",
    "warnings.filterwarnings(action='once')\n",
    "# 设置绘图选项:\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set(font='SimHei')  # 解决Seaborn中文显示问题\n",
    "pd.set_option(\"display.max_columns\", 36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>省份/州</th>\n",
       "      <th>国家/地区</th>\n",
       "      <th>日期</th>\n",
       "      <th>确诊病例数</th>\n",
       "      <th>死亡人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  省份/州        国家/地区          日期  确诊病例数  死亡人数\n",
       "0   1  None  Afghanistan  2020-01-22    0.0   0.0\n",
       "1   2  None  Afghanistan  2020-01-23    0.0   0.0\n",
       "2   3  None  Afghanistan  2020-01-24    0.0   0.0\n",
       "3   4  None  Afghanistan  2020-01-25    0.0   0.0\n",
       "4   5  None  Afghanistan  2020-01-26    0.0   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>确诊病例数</th>\n",
       "      <th>死亡人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35995.000000</td>\n",
       "      <td>35995.000000</td>\n",
       "      <td>35995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17998.000000</td>\n",
       "      <td>3683.508737</td>\n",
       "      <td>243.560217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10391.005806</td>\n",
       "      <td>18986.978708</td>\n",
       "      <td>1832.966999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8999.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26996.500000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35995.000000</td>\n",
       "      <td>345813.000000</td>\n",
       "      <td>33998.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID          确诊病例数          死亡人数\n",
       "count  35995.000000   35995.000000  35995.000000\n",
       "mean   17998.000000    3683.508737    243.560217\n",
       "std    10391.005806   18986.978708   1832.966999\n",
       "min        1.000000       0.000000      0.000000\n",
       "25%     8999.500000       0.000000      0.000000\n",
       "50%    17998.000000      19.000000      0.000000\n",
       "75%    26996.500000     543.000000      7.000000\n",
       "max    35995.000000  345813.000000  33998.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "国家/地区数量:  184\n",
      "数据日期截至 2020-05-15 开始于 2020-01-22 , 总共有 115 天\n",
      "包含有省份信息的国家:  ['Australia' 'Canada' 'China' 'Denmark' 'France' 'Netherlands' 'US'\n",
      " 'United Kingdom']\n"
     ]
    }
   ],
   "source": [
    "submission_example = pd.read_csv(\"C:/Users/Auror/Desktop/covid19/submission.csv\")\n",
    "test = pd.read_csv(\"C:/Users/Auror/Desktop/covid19/test.csv\")\n",
    "train = pd.read_csv(\"C:/Users/Auror/Desktop/covid19/train.csv\")\n",
    "train.Province_State.fillna(\"None\", inplace=True)\n",
    "#将列名翻译为中文\n",
    "train.columns = ['ID','省份/州','国家/地区','日期','确诊病例数','死亡人数']\n",
    "\n",
    "display(train.head(5))\n",
    "display(train.describe())\n",
    "print(\"国家/地区数量: \", train['国家/地区'].nunique())\n",
    "print(\"数据日期截至\", max(train['日期']), \"开始于\", min(train['日期']), \", 总共有\", train['日期'].nunique(), \"天\")\n",
    "print(\"包含有省份信息的国家: \", train.loc[train['省份/州']!='None']['国家/地区'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>预测ID</th>\n",
       "      <th>省份/州</th>\n",
       "      <th>国家/地区</th>\n",
       "      <th>日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-04-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-04-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-04-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   预测ID 省份/州        国家/地区          日期\n",
       "0     1  NaN  Afghanistan  2020-04-02\n",
       "1     2  NaN  Afghanistan  2020-04-03\n",
       "2     3  NaN  Afghanistan  2020-04-04\n",
       "3     4  NaN  Afghanistan  2020-04-05\n",
       "4     5  NaN  Afghanistan  2020-04-06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>预测ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13459.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6730.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3885.422971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3365.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6730.000000</td>\n",
       "    </tr>\n",
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       "      <th>75%</th>\n",
       "      <td>10094.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13459.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               预测ID\n",
       "count  13459.000000\n",
       "mean    6730.000000\n",
       "std     3885.422971\n",
       "min        1.000000\n",
       "25%     3365.500000\n",
       "50%     6730.000000\n",
       "75%    10094.500000\n",
       "max    13459.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.columns = ['预测ID','省份/州','国家/地区','日期']\n",
    "\n",
    "display(test.head(5))\n",
    "display(test.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '日期')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t25k1a1bp69lsNrKzs4mNjWXlypV8/vnn3HPPPaXf93q9pKenl1lz++/cbje3334748aNA3RMIGKGgG7E/ykrK4vc3Fxuu+02oOQf7r59+/jjjz+48MILqV+/PvXr18fhcLBr1y6aNWtmbmCpde6//35WrFhRZmmMv5YT+2tJjfz8fM4++2zuuusu7r777jLPb9q0KcXFxTz11FOMHDmy9PHzzz+/9M9/LdkBcMMNN/Dvf/+bqVOnAiX3YRUXF7N48WIKCgpKtwsJCeH5558vHXBfeOEFtm3bxgUXXFA6u/o/zZ49mwsuuAAoGZBPOukkBgwYUPr9wsJCRo8ezcUXX1zmeW63G5fLxfvvv8+0adPo2bNn6fd27tzJfffdx6hRo0ofe+qpp2jXrh2PPPIIHo+HkSNHEhcXh91uZ8CAAfTt25c333yTDRs20L1799LnnXPOOZx99tm0bdu2zP69Xm+ZNVNFRETMUNXHBIWFhUyaNAmbzcZrr71GaGho6TY9e/akX79+eL1ewsPDGTt2bJkr3jp37swff/xx2OyGYZRpgHVMIFL9LMaRZpgIELfddhvDhw/n1FNPZdCgQcybN690qYUePXqwZMkS9u3bx80330xGRgbDhg3j66+/JiQkxOzoUgcZhkFxcXGl/fxlZWWV+VQYYMOGDURFRdGwYcPjft2CggKsVithYWGH3SYtLe2Ik8Idjz/++IPmzZuXTjYjIiJSW53oMcFvv/1Gu3btqnxtbB0TiFS/GtWIn3baaSxbtqx0NsWmTZsyffp0/H4/d911F3v27CEnJ4err766zBlGERERERERkUBRIxpxERERERERkdpCs5mJiIiIiIiIVCM14iIiIiIiIiLVKOBnTc/OduL3n/jV8/HxEWRmFlRCotpDNSlPNSlL9ShPNSlPNTnIarUQG3v4pQblxFTWMQHo5/afVI/yVJPyVJPyVJOyVI+DjnZMEPCNuN9vVNqgW1mvU5uoJuWpJmWpHuWpJuWpJlIdKvOY4K/Xk4NUj/JUk/JUk/JUk7JUj4rRpekiIiIiIiIi1UiNuIiIiIiIiEg1UiMuIiIiIiIiUo0C/h7xf/L5vGRnp+P1uo/peWlpVvx+fxWlqplqQ03sdgexsYnYbDXuR1lERE6QYRgUFOTichXg9/uO6bm1YQysTIFaD6vVRmhoBBER0VgsFrPjiIhUmhrXvWRnpxMSEkZ4eNIx/Ydst1vxegNvgDFTTa+JYRg4nXlkZ6eTkNDA7DgiIlLNsrPTsVgsxMXVx2az67jgBARiPQzDwOfzkp+fQ3Z2OnFx9cyOJCJSaWrcpeler5vw8Ch9KipYLBbCw6OO+eoIERGpHdzuImJi4rHbg3RcUAtZLBbs9iBiYuJxu4vMjiMiUqlqXCMOaLCVUvpZEBGpywwslhp5KCPHoOTvWMshiUjtotFLREREREREpBqpEa9kr7zyIpdeenHp19deewWff/5ple7zwQfv49Zbbyj9+oILzuPnn3+q0n2KiIjIQRr/RUTkWKgRrwJbt25hzZrfq3Wfy5f/yIED+6t1nyIiInKQxn8REakoNeJVIDw8nI8//qBa9xkSEsL8+R9X6z5FRETkII3/IiJSUWrEq0DfvgNYsuQ78vLySh+bO3cOI0YMYdSoESxb9gNQchnbf//7BDfeOJnBg/vz9NOPl24/Z86rjBgxhH/961yWLv3+qPs855zBfPbZJ/h8B9dR9fv9PPPMUwwbNohLLx3F+vVrgZJL2WbNepmJE8cxaFA/5s59HQCv18uMGU9w/vkDGT36X6xZ80el1ENERKQuqCnj/1VXjS83/j/11GMa/0VEqlGNW0e8JoiOjqFnzzNZsOBzAHw+H198MZ9Zs94iMzODKVOuZtasuQB89tknPP308yQkJHLRReczfvxE1q5dw+rVq3jzzXmkpx9g8uQr6dHjDOz2w/91tWiRwpYtm1m2bGnpY5999glbtmzinXc+Ys2a37n77tuYO/d9AD755EOef34m2dk5XH/9JEaPHsunn35EXl4u8+Z9ym+//cJjjz3E7NlvVWGlREREao+aMv4/88yL5OXl/mP8z9P4LyJSjdSIV5Hzz/8Xjz76EDExMbzwwjOMGjWWqKgooqKiaNeuPb/99gsAPXv2om3b9gDExcXjdDr56aeVrF+/jpEjhwFQVFRERkY6SUkNjrjPYcP+VeaSuOXLf+C884YRHBxMt26nEB4ewbZtWwAYOHAIycmNqV+/IU6nE4CfflrB6tU/8a9/nQtAcXERXq/3iAcAIiI1mWEYGIBVSyFKJakJ43+jRsk0bNiozPj/888/sWKFxn8RqeMMP1TTspj6H7aKdOrUBZvNyubNG7HZ7Pz9GM9isZSuf92oUXKZx0sY/Pvfl3HJJeMAyM/PJzQ09Kj77NOnP88++zQul+sQr/nXn8vu9+/fNwy4+ebb6d///wDIy8vFZrNV+D2LiNQ0b329maz8Yq4d0dHsKFJL1NTx/5Zb7qBv37MBjf8iUjfZc1YQ/esosk/9Bn9Y8yrfn+4Rr0LDhl2A0+nk6qsns3Dhl+Tn57Nz5w7WrVtDp05dDvu87t1PZdGir3E6C8jISGfkyGEUFBQcdX8Oh4OBA4fgchUC0KNHT+bP/xi3280vv6ymoCCfFi1SgLID8N/3u2DB53g8HrZs2czo0Rfg9/uP782LiAS4rLwivv1lL3FRwWZHkVqmJo7/X375mcZ/Eam7DB8RG6Zi2ELwB9evll3qjHgVOuecQTz//H+x2Wycc85gLr30YhyOYG677R7i4uIP+7zTTz+TjRs3MHbsSGw2G9dffzMxMTEcOLCfW265ntmz3z7sc88/fwRvvVUy+cqQIUPZsWM7F110PtHRMUyb9jAOh+Owzx06dDg7d27nwguHEhYWxr33Ttcn4iJSa32zeg9+w+Ds7o3NjiK1TE0c/3ft2qHxX0TqrJC9cwjK/528jrPAFlYt+7QYhmFUy56OU2ZmAX7/wYj79+8kKanpMb+O3W7F6635n+4+//x/mTTpukp5rdpSk+P9mTiUxMRI0tPzK+W1agPVozzVpLyaWBNXsZebnv+RDs3juHpYh0p7XavVQnx8RKW9npRVWccEUPPGwMoc/w8l0OtRmWN9RdXE/9uqmmpSnmpSVk2sh8WTTdwPXfFGtCO322dQSfPGHO2YQJem1yAej4e+ffubHUNEpMZb8nsqrmIv55zaxOwoIkel8V9EpOqEbf0PFk8OBW0eqbQmvCLUiNcgQUFBpTOsiojI8fH5/SxctZvWydG0aBhldhyRo9L4LyJSNWwF6wjd8xJFyZfhi6zeiVvViIuISJ2yemM6mXlFnHOazob/xel0smTJEtatW2d2FBERkephGERsvA3DFokz5a5q370acRERqTMMw+DLFbuoHxdG55YJZscJCG63m8svv5w1a9bw+OOPM2vWLFJTUxk5ciQjR47kzTffLN125syZDB8+nAkTJpCRkQFQKduKiIhUN0fapziyFuNseReG4/ATaVYVNeIiIlJnbNiVw479+ZxzSmOs1XgfWCDbunUrEyZM4Oqrr+bWW29lyZIl3HXXXUyaNIm3336bhQsXsm/fPn7++WcWLVrEvHnzuPzyy5kxYwbACW8rIiJS7XwuIjbdgTeiPUWNxpsSQcuXiYhInfHZsh1Ehzvo2THJ7CgBo23btrRt25atW7fy/PPPc/755/Pwww/Tu3dvAHr27MnKlSvZvXs35557LjabjR49evDQQw/h8/lYv379CW07bNiwCmf95+yzaWlW7PbjP6dwIs+tjQK5HlarlcTEyGrfrxn7DHSqSXmqSVk1oh5/PAVFu6D/tyTWjzUlghrxauJyuXA4HIdcl9Pr9WK3H/yryMvLZe/ePRWamMXlchEaGlqpWUVEaqPtqXms25HNhX1TCLJrjeR/WrFiBTt27CAsLIz69euXPh4VFUVaWhpOp5N27doBYLFYKCwsxOVynfC2x+Kfy5f5/f7jXnIr0Jfr+ktGRgYJCVV/G0Wg18Pv91f7kkg1cRmmqqaalKealFUT6mF17SJu7X8orj+CfGs3qKK8Wr6sGu3cuQOAoqIiiouLKC4uxuksAOC552bw0Ufvl3tOVlYm48ePISMjvfSxTz/9iGuumUh+/tF/KK68chz79u095PcKC528+eZsDrVUfHZ2Ft9++w0AGzas55tvFvLaay/x9dcLeOWVFyksLOSOO24mPf3YDpIqU2rqPtxut2n7F5Ha5bNlOwkLttOnSyOzowSk0aNHM2PGDJ599tky//c6nU4MwyAiIgKXy1X6eEFBAaGhoSe8bW3213GB1+vF5/Mddjuv14vff+gm+I47bmLp0u8qtL/Zs19h/vyPD/k9jfUiIiUiNt0FWHC2mmZqDjXilWTjxg3MmfMKfr+fN9+czSWXXMS//nUuV101ng0b1rN48Td8++3XjBo1okxDHhcXz9ixl/H22yWT1hQWOvnii8+48cZbefHF58rt56OP5jF+/CVce+0VXHvtFfj9fu67706uvfYKrrlmInPnzinddtWqFWzatIEXXvgvY8dexK233sDrr8/6cz+FfP31VwC0atWabdu2YLfb8fv95OXl4nA4SE3dS2JivSqs2pFlZmbyxBMPm7Z/Eak99mY4+XlTOgO6JxMarIvB/u69997j8ccfByAnJ4fY2Fiio6NJTU0FYO3atSQnJ9OlSxeWL18OwI4dO4iNjcVms53wtrXV348L3nhjFldddRmDB/fnsstGc/XVExgxYgiXXHIhV189gauvHs/atX8c8nUuumgUn38+v0L7LCgoOOyHGxrrRUQgKOt7gtM+orDZDfhDG5uaRUcjlcAwDGbPfoV77pmG1WplwoQriYiIIDo6htNOO4MpU67iqaeeo3nzFlx//SQGDTq3zPPPPnsg/fv/H36/n+nT72PkyNH063c2a9f+wbRp9zBu3OXExyfwwgvP0LRpU4YMGcq//nXRIbP8/RP1Dz6YxyWXXMopp5zGihXLeeSRpwC44ILzeOaZF7H8OVHRq6/OZMWKZWzatIEmTZoRERHBnDmvsn//fq6+egIAHTt2ZtKk66qifIfVoUNHNm5cz/ffL6ZXrz7Vum8RqV0+X7YTR5CVAd3NHXQD0fnnn88tt9zCxRdfTHBwMPfccw/bt2/n2muvpWvXrqxZs4YHHniAkJAQnn32WaZPn87q1asZO3YsAOPHjz+hbWujfx4XjBt3OePGXc748WN44YVXCA4O4ZVXXqRVqzblxrcrr7wMn8+Hx+MhODgYw/BjsVi54opxf762H6fTydy55a+y8/v9eDyeQ2Y62lhvsVjo0KGTxnoRqb38XiI23oIvpCmFzaaYnaZmN+I//JHK0t9TK7StxQLHcgXcmZ0a0LNjgwptu2bN75x8cjdCQkJKH1uxYjn33TedsLBw/vWvkdx9922EhYWSm5vLlClX06BBQ2677S6uueYKgoKCOO+8YSxY8DmNGzehU6cujBs3ipdfnsMHH7zH88/P4KGHHickJITNmzfhdDr5+usvcTiCKSwsJCsrk+TkJvj9PqxWGzNmPM8vv6xm9eqVXHvtFLKyMsnNzeGVV16ka9duxMWVnZ7/8suvIisrC7/fT4sWKZxzzmB+//1XrrnmOs49dxhXXDGOK66YVPHiVaLzzx/Bgw/ep8FZRI5bRo6LFesOMKB7MhGhQWbHCTgOh4Onn366zGMpKSk0b96ctWvXMnnyZMLDwwGYM2cOixYtYtCgQXTr1g2AAQMGnPC2lSV431xC9r1RoW2P9bigqOElFDccXaFtD3VcsG7dGvbu3cP111+DzWajU6cuQMlVCA89dB/Tpj1McHAIL774Gjk5OUyefAUzZ86qeEAgIyONsLCwQ37vaGP9lVdqrBeR2i1kzyvYC9aR2+kNsJk/x1aNbsQDxYYN60sHVID169eyadN6Xn75f2zfvo2rr57M2WefQ9eu3fj9918555zBvPDCMwQHh/Dyy3N45ZUXMQyD666bisfj5u67b+WOO+7l22+/oVu3Uxg37nIArrlmCp9++hE+n5dhwy4A4KefVvL5559yzz0H73Hwer088cQjdOjQCYCvv17A//3fQIKDg9mxY3u5CePuu+9OmjdvQYMGDYiJieWLL+azbt0aBg4cApRMtPP3yeQqaunS75g9+xVsNjtt27ZnypSpfP75p6Sm7mPChCuBkrPzd9xxLzNmPEFYWCgJCfXIzs4iPj6e++//D3a7/bj2LSLyl8+W78Rigf87RWfDj0VKSgopKSllHnM4HAwcOLDSt61t/nlcYBgGL774HI8++jTt2nVg0qQJ2Gw29u3bw5tvzubf/x5PcHDI4V+wgjZu3MCBAwdKx9i/01gvInWZxZ1J+NYHccf1wV3vPLPjADW8Ee/ZseJnratyNtDi4qLST73dbjePPfYfGjVqzI033sqNN07G5/PxxRfzWbLkO5zOAn78cSkNGpTNbbfbadKkKffccxsPPPAwe/fu4d135/LUU8/h9XoB+Pjj93nrrTe49NIJDBnSn0aNGpObm0NRURFXXDEOl6uQk0/uzg033MKjjz7F22+/wYED+3n//XeZNGkKW7ZsomXL1uXyT558I1FRUdx447XMmPECXbp05YMP3mPjxvVkZWUe971jn376EWPHjqdXrz588cX8w05EAxAREcEdd9zLddddxTvvfMSoUSOOa58iIn+3P6uQJb+l0vfkRsRFnXijI4GtuOHoCp+1rq7jAoAXX3yOhIQEsrIyWbbsBzp3PhmATz75kAcffIzmzVuc8D5/++0XmjVrTv36SSxe/A19+vQv8/2jjfX16tU/zCsfmcZ6EakJwrdOx+LLp6DNIyWXRAUATdZWCZKSGrB37x6g5AzAo48+TUjIwcsdioqK6Nq1G4MHn8vJJ3fnootGlXuNoqIi0tIOcMstdzF//sfMmfMqM2b8j48//oBnnnkSm81Gbm4uAwcOISoqioSEesycOYu+fQcQExPDgw8+yg033ILFUvJX2rBhyazAUVHR3HrrXfzxx2/Mm/cOnTt3LTdIvvrqi/z73xeTk5PNjTdey+TJV3LddVMpKChg9epVtG3b7rjqMn78lSxY8BlTplzNgQP7sVrL/rgVFRWV/rlhw0ZYrVYaNGiI3W4vM9nMXx9EiIgcqw+/30aQ3cq5ZzQzO4rUIX8/LgC44IKRXH31FBYvXsSDD97L0KHDAbjqqsmV0oQbhsFLL73ARReN5qKLRjNz5vMUFBSU2UZjvYjUVbb83wnZ8yqu5In4ItqaHaeUGvFKcNppZ7B48TelXyckJLBv3x6mTJlEYaGTAwdSCQ+PwOfzYxjGIT8tnjnzOVasWMYPP3xHaGgoTz/9PG+8MYs1a35n4sRJWCwWxo+/guzsLBo3boLFYmHv3j18881Crr76OqZMuZqMjIzSCdj+Ehoaysknd6dPn/6cccaZhIaGlluf/JZb7iQxMZEJE65k6NARDB48FKvVSp8+/Xj66cc466w+x1WXH39cwt13T+PJJ59lwYLP2bt3D3Z7EDk5OQAsWbL4qK+xdesWkpIqdtWDiMjf7difx6oNafzfKY2JDneYHUfqkPLHBYkkJCTQuXNXevfuR5MmTctsn5eXy7p1a0q/PtZl3V588TmSkxtz8sndadQomfPPH8Edd9xUZgm5o431vXv3Pa73qrFeRAKaYRCx6U6MoFgKU243O00ZasQrQWRkJE2aNOXbb78ufaxBg4bMmPE8LVu25ocfljJkyHm0aXMSDRs2omPHzmWev3HjBnr27MWIERfSs2dvGjduwlVXjcdms/HYYzOIiChZCN7v97Nmze80adIMw/Dz9ttvMnbsOHr0OIPrr7+Z0NCQMldaGEbJYL5nz24ef/w/XH31ZACuv/4mPB4PVuvBe8VvuOFmfv55NY899hDbt28lPz+foqIi/H6D7Ozs0u3mzp3D8uU/VqguycmNmTz5Sq699gpat25DUlIDTjnlNDZv3siTTz5y2PXP/1JcXMRLLz3P6NH/rtD+RET+7v3vthERGsQ5pzYxO4rUMYc6Lli69Du++uoLrr/+ZgBsNhsuVyEAq1at5K23Dk4y5/V6yMjIYOzYi8r9uvDC89m4cQMATmcBjzzyINu2beGmmw4eYI4cOYbWrU9iwoRLWLVqRenjRxrrs7KySrfTWC8itUVQ5tc4sr6jsMWtGEGxZscpw2Ic68eu1SwzswC//2DE/ft3kpTU9AjPOLSqvBcMDi5VMnbsZdhsNubOfZ3Ro8eydesWXn75Bf7znyfYtGkDW7duYfXqVcTFxTFpUsm0+Rs2rKNFi5YYhsEtt9xA164nM2TI0HL3Zi9e/A0rVizj1lvvYvTof5VZuiQ1dR+XXz6WG264hQEDzgHg4YenMXDgEObOncPEiZNo1ark/vBly5by3/8+yYQJVzBgwEDWrVvDggWfk5OTzeTJU9m8eSMffvgezZq1YPjwC5g6dTIjR47h/PNHsGDB53To0IlGjap+7delS78nMbEebdqcdMTtjvdn4lASEyNJT8+vlNeqDVSP8lST8gKxJut3ZPHY278ysl/Lam3ErVYL8fER1ba/uqayjgmgeo8LPvrofb7//lseeugxwsNLfj5+//1XZsx4Are7mLCwcK688hpOPrn7Me3jxRefA2DChCsPOdnZN998xZYtm7nyymuOOtbfdNN1XHTR6Dox1ldUIP7fZjbVpDzVpKyAqofhI3b5WVh8BWSd8RNYq/fquKMdE6gRrwZ+v7/MPVOZmRnExycc8+sYhkFBQQGRkZGH/H5ubg7R0TEVfr2/arJ58yaA0kZ9//79ZGdnll7Cnpubg9PppGHDRhQWOgkLq9zlZk6UGvGqo3qUp5qUF2g1MQyD6XNWk1NQzMNX9iDIbjv6kyqJGvGqVZMa8b/zeDwYhoHDYd4tEkcb653OPHJz8+vEWF9RgfZ/WyBQTcpTTcoKpHoE75tL1NqryOv4KsVJF1T7/o92TFCjZ02vKf45ccnxNOFQsrTI4Zpw4Jia8L/7a1D+S1JSEklJSWVe96/XDrSBWUTkn37elM721DzGDTqpWptwkcMJCjJ//fqKjPXh4VGAxnoRqQV8RYRvfRBPZFeK6wfmCg1qxEVEpNbw+f28/902GsSH0bNj0tGfIDWcBcPwl64YIrWTYfiBwFhuSERqhtDdM7EV7Sa//fMQoGNEYKYSERE5Dj/8sZ/9WYWM6JWCzaohrrZzOELIycnA6/Uc80zjEvgMw8Dr9ZCTk4HDEXL0J4iIADbnZsK2P4Y7fgCeuN5mxzksnREXEZFawe3x8fHS7aQ0jOLk1sd3C5DULLGxiRQU5JKVdQC/33dMz7VarYdcTrSuCtR6WK02QkMjiIiINjuKiNQAFk8WUb9eBBY7+W2fNDvOEakRDzAZGRkkJOgAUkTkWH2zeg/Z+cVccV47LBZdxloXlMydEkNkZMwxPzeQJhQKBKqHiNR4fjdRv43F5tpNTvf5+EObmZ3oiHTdXiXauXMHAF6vF5/v8J/Me73ew37qfMcdN7F06XcV2t/s2a8wf/7Hh/zehg3r+eabhbz22kt8/fUCXnnlRQoLC7njjptJT0+r0OtXhdTUfbjdbtP2LyK1k7PIw2fLdtKxRTxtmgTWOqEiIiJSxQyDiPU34MheQn77Z/HG9DA70VHpjHgl2bhxA++++yZ33nk/b7wxix9++J69e/dSv359QkJCOXBgP2FhYURGRuH1erjuuql07Ni53OtcdNEoPv98PmeeefT7GQoKCoiLiz/k91q1as2SJYsJCQnB7/eTl5eLw+EgNXVvufXJq1NmZiazZr3M7bffY1oGEal9Pv1hB65iL//q3cLsKCIiIlLNQnc8Rei+13E2v5niBhebHadC1IhXAsMwmD37Fe65ZxpWq5Vx4y5n3LjLGT9+DC+88ArBwSG88sqLtGrVhl69+pR57pVXXobP58Pj8RAcHFw6++sVV4z787X9OJ1O5s59v9x+/X4/Ho/nkJlefXUmK1YsY9OmDTRp0oyIiAjmzHmV/fv3c/XVEwDo3LkLV101uVJrcTQdOnRk48b1fP/94nK1EBE5HnvSC/j6pz306tKQJvUPv8SjiIiI1D7B+94kYst9FCVdQGHKnWbHqTA14pVgzZrfOfnkboSEHJzRc926Nezdu4frr78Gm81Gp05dAMjJyeGhh+5j2rSHCQ4O4cUXXyMnJ4fJk69g5sxZx7TfjIw0wsLCDvm9yy+/iqysLPx+Py1apHDOOYP5/fdfueaa6zj33GFcccU4rrrqmuN9yyfk/PNH8OCD96kRF5ETZhgGcxduIjTYxoheOhsuIiJSlzjSFxC57lrccX3Jb/+/gF2q7FBqdCPu2fQDno3fV2hbi8VyTEubBLXpRVDrnhXadsOG9aWNNpQcGL744nM8+ujTtGvXgUmTJmCz2di3bw9vvjmbf/97PMHBJ74Mx8aNGzhw4AATJlxZ7nv33XcnzZu3oEGDBsTExPLFF/NZt24NAwcOAUrqYbfb8XqPbYbUpUu/Y/bsV7DZ7LRt254pU6by+eefkpq6rzTHBRecxx133MuMGU8QFhZKQkI9srOziI+P5/77/4Pdbsdur9E/eiISIFZtSGPDrhzGntOGyDCH2XFERESkmthzVxH1+6V4IzqS1/kNsNas44Ca85FBACsuLipzNvzFF58jISGBrKxMli37gc6dTwbgk08+5Lbb7qZnz7NOeJ+//fYLzZo1p02bk1i8+Jty3588+UZGj/43K1cuZ8iQoYwbN4EePc5g48b1ZGVlHvd94p9++hFjx47nf/97ldat2xxxqZOIiAjuuusB1q1bw9NPP8+6dWuPa58iIodS5PbyzqItNK0fSe/ODc2OIyIiItXE4s4k6tfR+IPrkdt1Hoa95t2aVqNPSwa17lnhs9Z2u/WYz/5WVFJSA/bu3UPTps0AuOCCkYCFZ555kuXLf+Cll+awcOGXXHXVZJo3P/FLJw3D4KWXXmD8+CuoXz+Jm2+eQvfupxEREVG6zauvvsjPP6/Gbrdx443XsmPHNj788AumTLma1atX0bZtu+Pa9/jxVzJnziu8//47dO3aDau17Gc5RUVFpX9u2LARVquVBg0aYrfby1yR4PV6j2v/IiJ/+fTHHWTnF3P1sA5YrVquTEREpE4wDCLXX4fVk032yR9iBJs3EfWJ0BnxSnDaaWeUOSudkJBIQkICnTt3pXfvfjRp0rTM9nl5uaxbt6b062O5ZB5KzrgnJzfm5JO706hRMuefP4I77rgJl8tVus0tt9xJYmIiEyZcydChIxg8eChWq5U+ffrx9NOPcdZZfY7rvf744xLuvnsaTz75LAsWfM7evXuw24PIyckBYMmSxUd9ja1bt5CU1OC49i8iAnAgq5CvVu6mZ8ckWjaKNjuOiIiIVJPg1LkEp32Ks+Xd+CI7mB3nuNXoM+KBIjIykiZNmvLtt1/Tt+8AoORe6q+++oInn3wWAJvNhstVCMCqVStZvPgbpk17GACv10NGRgZjx15U7rWLioqZPv0R2rQ5CaezgGefnUFmZjoPPfR46TYjR44hPT2dCRMu4YYbbuGUU04D4IYbbubDD99n4cIv6dKlK/n5+RQVFeH3G2RnZwPNAZg7dw4tWrSkR48zjvpek5MbM3nyldjtdlq3bkNSUgPCwsL54IN3efLJR4iPTzji84uLi3jppee58877j7ovEZHDefubzQTZrVzQO8XsKCIiIlJNrK6dRGy4BXdMT1xNrzU7zgmxGMd6OraaZWYW4PcfjLh//06Skpoe4RmHVpWXpsPBJczGjr2Mjz56n++//5aHHnqM8PCSy8V///1XZsx4Are7mLCwcK688hpOPrn7Me3jxRefA2DChCsPOdnZN998xZYtm7nyymtYt24NCxZ8Tk5ONpMnT2Xz5o18+OF7NGvWguHDL2Dq1MmMHn0J5547nAULPqdDh040apR84oU4iqVLvycxsR5t2pxUaa95vD8Th5KYGEl6en6lvFZtoHqUp5qUV901+WNbJk+9+xsX9k1h0GmV82+/slitFuLjI46+oRyXfx4TnAj9Wy5L9ShPNSlPNSlPNSmrSuth+Ij+6Vzs+b+TffqP+EMD6xjgn452TKBGvAp4PB4Mw8DhMG/mvs2bNwHQqlVrAPbv3092diZt27YHIDc3h+JiF/XqNaCw0ElYWLhpWU+UGvGqo3qUp5qUV5018fr83PPKSgzDYNrlp2G3BdYdVmrEq5Ya8aqjepSnmpSnmpSnmpRVlfUI2f0KkRtuIK/d8xQ3uqRK9lGZjnZMoEvTq0BQUJDZEUob8L8kJSWRlJRU+nV0dAx2exxer79GN+EiUrd8s3oP+7MKmXJBp4BrwkVERKRqWDw5hG+djjumJ8UNx5gdp1JU61HMDTfcwAcffHDCrxPgJ/GlGulnQaTuyHO6+eSH7XRsEU/nlkeej0JERERqj7Btj2DxZOFs8zBYasdKKdXWiH/++ecsWrTohF/HbnfgdOapARMMw8DpzMNuN+8WABGpPvN/3EGx28/F/VuaHUVERESqic25mdDdL1LU6N94ozqbHafSVMul6enp6bzyyiuMGjXqhF8rNjaR7Ox0Cgpyjul5VqsVv7967hGvKWpDTex2B7GxiWbHEJEqlpHrYvGvezmzUxIN4nU7jYiISF0RvukODGsozpS7zY5SqaqlEb/33nu5/fbbWbZs2TE/91A3uCclxVZGLJFDSkyMNDtCQFE9ylNNyqvqmsz9ZgsWi4Vx53UkMTa0SvclIiIigSEo42uCMxZQ0GoaRnA9s+NUqipvxN977z1SUlLo3r37cTXilTVDqmY0LE81KU81KUv1KE81Ka+qa7Ivw8k3P+3i7O6NwesN6Ppr1nQREZFKYviI2HwXvtDmuJpcZXaaSlfljfjXX39NXl4eY8eOZe/evTgcDqKjo+nfv39V71pERGqBD5dswxFkY/Dpgb1eqIiIiFSe4NR3sBesI7fTbLAGmx2n0lV5I/7iiy+W/vmZZ56hUaNGasJFRKRCtqfmsXpjOkN7NiMqTBMzioiI1An+YsK3PoQnsivueuebnaZKVOs64pMnT67O3YmISA334ZJthIfYOefUJmZHERERkWoSuucVbEW7yG/3DFiqdcXtalM735WIiNR42/blsWZbFgNPa0JocLV+biwiIiImsXjzCNv2GO64Pnji+5odp8qoERcRkYA0/8cdhIfY6XdystlRREREpJqE7nwWqycTZ8t7zY5SpdSIi4hIwNl1IJ9ft2RwdvfGOhsuIiJSR1jcGYTufJbiesPwRnczO06VUiMuIiIB59MfdxAabGNAd50NFxERqStCd/0Pq68AZ8qdZkepcmrERUQkoOxNL2D1xnT6d0smLCTI7DgiIiJSHbwFhO6eSXHiEHwRbcxOU+XUiIuISECZv2wnwUE2zu7e2OwoIiIiUk1C983B6s2hsNn1ZkepFmrERUQkYKRmOlm57gD9Tm5EpNYNFxERqRv8HkJ3Poc75gy8MaeZnaZaqBEXEZGAMf/HHQQFWbVuuIiISB0SvH8etqLduOrI2XBQIy4iIgEiNdPJ8nUH6HdyMlHhOhsuIiJSJxgGYTtn4A1vizvh/8xOU23UiIuISECY/+MOguxWBupsuIiISJ3hyFiAvWAdhc2mgKXutKd1552KiEjA0tlwERGROsgwCNv2ML6QZIqTLjQ7TbWymx1AREREZ8NFRETqHkfaxwTl/Uxeu+fBWreWLNUZcRERMZXOhouIiNRBfg/hWx7AG34SxQ1HmZ2m2umMuIiImGr+jzt1NlxERKSOCdn3OvbCLeR2fhssNrPjVDudERcREdMcyC5k+br99O3aSGfDRURE6gpfIWFbH8YT0wN34iCz05hCZ8RFRMQ0ny/bic2qs+EiIiJ1SeiuF7C595PXaTZYLGbHMYUacRERMUVGrosf1+ynT5dGREcEmx2nziooKOCmm27C7XaTm5vL9OnT2bBhAzNnziQhIQGAhx56iMaNGzNz5ky++OIL4uLieOSRR0hISCA1NZXrr78egKFDhzJmzBiAY9pWRETqDqtrN2E7nqI4YRDe2NPNjmMaNeIiImKKL5bvAmBQD50NN9PHH3/M0KFDGTx4MIsWLeKZZ54hNjaWxx9/nPbt25du9/PPP7No0SLmzZvHypUrmTFjBtOmTeOuu+5i0qRJ9OrVi8suu4y+ffuyf//+Cm/bsGFDE9+9iIhUK7+HqD8uA8OgoM1DZqcxlRpxERGpdtn5xSz5fR9ndmpAXFSI2XHqtL+flc7MzKRevXosW7aMXbt24XQ6Ofnkk7nrrrv44YcfOPfcc7HZbPTo0YOHHnoIn8/H+vXr6d27NwA9e/Zk5cqV7N69u8LbDhs2rMJZ4+MjKvW9JyZGVurr1XSqR3mqSXmqSXmqSVlHrMcvN0PuSuj5DvFNu1RbpkCkRlxERKrdFyt24vfD4B5NzY4if8rKyuK1117jpZde4tRTT2XQoJLJcy677DJWrlyJ0+mkXbt2AFgsFgoLC3G5XNSvX7/0NaKiokhLSzumbY9FZmYBfr9xom8VKDlQTE/Pr5TXqg1Uj/JUk/JUk/JUk7KOVA9H+hdEr38cV/LlFIQNglpeN6vVcsQPkNWIi4hItcrKK+L7X/dxevv6JMaEmh1HAI/Hw9SpU5k6dSqNGjUiPj4ey5+T57Rp04YtW7YQERGBy+UqfU5BQQGhoaG43e7Sx5xOJ4ZhHNO2IiJS+1ldu4hccyWeyM4UtK7bl6T/RcuXiYhItXr/u234DTj/zOZmRxHA5/MxdepU+vfvT//+/dmzZw+XX345Pp8Pp9PJ0qVL6dChA126dGH58uUA7Nixg9jYWGw2G9HR0aSmpgKwdu1akpOTj2lbERGp5bwFRP86CvCT12kW2HRLGuiMuIiIVKNt+/JYtnY/g3s0JUFnwwPC+++/z+LFi0lPT+ezzz6jYcOG9OrVi8GDBxMcHMyoUaPo1KkTPp+PZ599lunTp7N69WrGjh0LwPjx47n22mvp2rUra9as4YEHHiAkJKTC24qISC1m+IlacwW2grXkdX0Xf1iK2YkChsUI8OvCKut+MN2/UZ5qUp5qUpbqUZ5qUl5Fa2IYBv9582fSsgr5z5WnExpc+z4LPtr9YDWd2+1m0aJFJCYm0q1bt9LHt27dytq1a+nduzfR0dHHvG1F6R7xqqN6lKealKealKealPXPeoRvvp+wHU9Q0OZhXE0mmZis+ukecRERCQirNqSxZU8u4wadVCub8LrA4XAwcODAco+npKSQkpJy3NuKiEjtE7zvLcJ2PIGr0WW4Gl9tdpyAo3vERUSkynm8PuYt3krjehGc2bGB2XFERESkClldu4lcfz3u2LMoOOlx+HMCUDlIjbiIiFS5BSt3k5FbxMX9WmK1ajAWERGpzcI33wVAfvsXwBpkcprApEZcRESqVFZeEfOX7aBrqwTaNoszO46IiIhUoaCs7wg58CGFzW7AH9rE7DgBS424iIhUqXcWbcEwYFT/VmZHERERkark9xCx4RZ8IU0pbDbF7DQBTbPliIhIlVm/M5tVG9IYdmZzLVcmIiJS2216HrtzPbmd54JN4/6R6Iy4iIhUCa/Pz9yFm0iIDmHgabo0TUREpDazuNPhj3txx/fDnTjE7DgBT424iIhUiUU/72VvhpNR/VvhCLKZHUdERESqUPiWB8DrpKD1I5olvQLUiIuISKXLK3Tz8dJtdGgeR5dWCWbHERERkSpkz/uFkL1zoM11+CLamB2nRlAjLiIile6jJdspdvsZNaAVFn0qLiIiUnsZBhEbb8UIiocOd5udpsZQIy4iIpVqT1oB3/26l34nN6JBfLjZcURERKQKBe9/j6Cc5Thb3guOGLPj1BhqxEVEpNIYhsFb32wmLNjO0DObmx1HREREqpLPSfjme/BEdqGo0SVmp6lR1IiLiEil+W1LJut3ZnP+mc2JCA0yO46IiIhUobDtT2Ir3kfBSY+CRROzHgs14iIiUim8Pj/vLNpMg/gw+nRtZHYcERERqULWon2E7XyGoqQL8Mb0MDtOjaNGXEREKsWin/dyINvFyH4tsds0vIiIiNRmYdseBsOHs+U9ZkepkXSkJCIiJ6ywyMOnP2ynXbNYOraINzuOiIiIVCGbcxMhe+fgSp6AP7SZ2XFqJDXiIiJywuYv20lhkZeL+rbUcmUiIiK1XPiWBzBsYRS2uNnsKDWWGnERETkhaVmFfP3THk7vkEST+pFmxxEREZEqZM9ZSXDaJ7iaXofhSDQ7To2lRlxERE7I61+sx2KBEb1amB1FREREqpJhEL7lPvyORAqbXmt2mhpNjbiIiBy3HfvzWPzzHs7u3pi4qBCz44iIiEgVCsr6Dkf2UpzNbwF7hNlxajQ14iIictze+3YrUeEOBvdoanYUERERqWKhO5/B76hHUfI4s6PUeGrERUTkuGzYmc36ndlc2L81YSF2s+OIiIhIFbIVbCA4cyGuxhPBGmx2nBpPjbiIiBwzwzD4aMk2oiMcDDqjmdlxREREpIqF7noewxqCK3mC2VFqBTXiIiJyzNbtzGbTnlzOPb0ZwUE2s+OIiIhIFbK4MwhJfYuiBqMwHAlmx6kV1IiLiMgx+etseGxkML06NzQ7joiIiFSx0N0vY/EX42p6jdlRag014iIickz+2JbF1r15nHdGM4LsGkZERERqNV8RoXteojjhHHzhrc1OU2voCEpERCrsr7PhCdEhnNmpgdlxREREpIqF7H8Pqzsdl9YNr1RqxEVEpMJ+2ZzBjv35nHtGM+w2DSEiIiK1muEjdOcMvBEd8cT2MjtNraL1ZkREpEL8foP3v9tKUlwYPTsmmR1HREREqpgj7RPszk3kdZwFFovZcWoVnc4QEZEK+WFNKqmZhYzo1QKbVcOHiIhIrWYYhG97DG9YK4rrn292mlpHR1IiInJUHq+Pj5dup3mDSLq1STQ7joiIiFQxR8aX2AvWUNh8Kli0VGllUyMuIiJHtejnvWTlFXNB7xQsujRNRESkdjMMwrY9ii+0GcVJF5qdplZSIy4iIkdUWOTls2U7ad8slrbN4syOIyIiIlUsKGsxQXmrKWx2PViDzI5TK6kRFxGRI/py5U4KXB7+1SfF7CgiIiJSDcK2P4YvuAFFDceYHaXWUiMuIiKHdSC7kC9X7Oa0dvVplhRldhwRERGpYo4Dn+DIXoqr2RSwBpsdp9ZSIy4iIodkGAZzF27GZrNwUd+WZscRERGRKmZxZxK54QY8kZ1xJU80O06tpnXERUTkkH7dnMEf2zIZ2a8lsZH6RFxERKS2i9hwExZPDvknf6x7w6uYzoiLiEg5xR4fc7/eTKPEcPp3SzY7joiIiFQxx4FPCDnwPoUtbsEX2cHsOLWeGnERESnns2U7ycwr4pKzW2O3aagQERGpzf5+SXphsxvNjlMn6NJ0EREp40BWIV+u2Mnp7evTpkms2XFERESkKhk+ItdepUvSq5kacRERKWUYBq9/tZEgu1UTtImIiNQB4VvuJzhjAfknPaFL0quRrjcUEZFSK9ensW5HNiN6pRAdoQnaREREarPgfW8RtuNpXMkTKGqsWdKrkxpxEREBoLDIy9vfbKZZUiR9uzYyO46IiIhUIXvuKiLXX4c79iwK2jxqdpw6p1oacafTyZIlS1i3bl117E5ERI7Dh0u2ked0M/acNlitFrPjiIiISFXxFhD12yX4gxuQ12mO7gs3QZU34m63m8svv5w1a9bw+OOPM2vWrKrepYiIHKOd+/NZ9PMe+p7ciOYNosyOIyIiIlUobOez2IpTyevwEoYj3uw4dVKVN+Jbt25lwoQJXH311dx6660sWbKkqncpIiLHwOvz89rn64kKczCiVwuz44iIiEgVsrgzCN35DMX1zsMbc5rZceqsKp81vW3btrRt25atW7fy/PPPM2zYsKrepYiIHIMvVuxiV1oB147oSFiILk0TERGpzcK2P47F58SZco/ZUeq0alu+bMWKFezYsYPY2GNbkzY+PqLSMiQmRlbaa9UWqkl5qklZqkd5takmO/fn8ekPOzirSyPO6Xn8Z8NrU01ERERqK6trF6G7X6ao4Rh8EW3MjlOnVVsjPnr0aM444wxuvPFGzjzzzAo/LzOzAL/fOOH9JyZGkp6ef8KvU5uoJuWpJmWpHuXVppr4/QZPvLGaEIeNf/VqftzvqzbV5ERZrZZK/QC5OhQUFHDTTTfhdrvJzc1l+vTpxMTEcP311wMwdOhQxowZA8DMmTP54osviIuL45FHHiEhIYHU1NQT3lZERKpH+NaHwGKhMOV2s6PUeVV+j/h7773H448/DkBOTg7R0dFVvUsREamAr1btZntqHmPObk1UmMPsOGKSjz/+mKFDh/Lqq69yzTXX8Mwzz3DXXXcxadIk3n77bRYuXMi+ffv4+eefWbRoEfPmzePyyy9nxowZACe8rYiIVA9bwXqCU9/C1fgK/CHJZsep86q8ET///PPZs2cPF198MU899RR33XVXVe9SRESOIj3HxYdLttG1VQKntq1ndhwx0ZgxYxg8eDAAmZmZJCYmsn79enr37o3FYqFnz56sXLmSH374gXPPPRebzUaPHj349ddf8fl8J7ytiIhUj7Dtj2HYwilsdqPZUYRquDTd4XDw9NNPV/VuRESkggzD4M2Fm7BaLVzyf22wWLRmuEBWVhavvfYaL730Er///nvp41FRUaSlpeF0OmnXrh0AFouFwsJCXC4X9evXP6Ftj0VlX/avuQ3KUj3KU03KU03KqxE1KdgOBz6Ak24koVGzKt1VjahHAKi2e8RFRCQw/LI5g9+3ZjKyX0tiI4PNjiMBwOPxMHXqVKZOnUpSUhJut7v0e06nE8MwiIiIwOVylT5eUFBAaGjoCW97LCpr3hjQ3Ab/pHqUp5qUp5qUV1NqErHhP4RgIyvhcvxVmLem1KM6HG3emCq/NF1ERAJHkdvL3K83kZwYwYDuuj9MwOfzMXXqVPr370///v2x2WxER0eTmpoKwNq1a0lOTqZLly4sX74coHQVlMrYVkREqpbFnU7I3tcpanAx/pCGZseRP+mMuIhIHfLpDzvIyivmykvaY7Pqs1iB999/n8WLF5Oens5nn31Gw4YNGT9+PNdeey1du3ZlzZo1PPDAA4SEhPDss88yffp0Vq9ezdixYwFOeFsREalaobv+B/5iXM2mmB1F/sZiHOt1YdVMy5dVHdWkPNWkLNWjvJpck70ZTu57dSVndEjissFtK+11a3JNKltNXL7scLZu3cratWvp3bt36YonbrebRYsWkZiYSLdu3Spt24rSpelVR/UoTzUpTzUpL+Br4i0gfkk7PHFnkdf5zSrfXcDXoxod7ZhAZ8RFROqIed9uITjIxgV9UsyOIjVASkoKKSllf1YcDgcDBw6s9G1FRKRqhO6dhdWbQ2Gz682OIv+g6xJFROqAzXty+G1rJoN6NCFSa4aLiIjUfoaP0F0v4I49E2/0KWankX9QIy4iUssZhsH7320jOtzBgO6NzY4jIiIi1cCR+Q22ot24Gl9hdhQ5BDXiIiK13JrtWWzancN5PZsRHGQzO46IiIhUg5C9s/EHJeBOHGx2FDkENeIiIrWY3zB4f/FWEmNC6NVZS5aIiIjUBZbiAzjSv6Co4Riw6pa0QKRGXESkFvtpQxq70goYdlYL7Db9ly8iIlIXhOx7E4vhpajRv82OIoehozIRkVrK5/fz4ffbSE4M57R29c2OIyIiItXB8BO6dzbumJ74wluZnUYOQ424iEgt9eMf+zmQ7WJ4rxZYLRaz44iIiEg1CMpeis21naLkS82OIkegRlxEpBbyeP188sN2mjeIpEvLBLPjiIiISDUJ2TsLvz2G4nrnmx1FjkCNuIhILfT9b/vIzCtmeK8WWHQ2XEREpE6wuDMJPvAJRQ1Ggi3U7DhyBGrERURqmWKPj/k/7qB14xjaN4szO46IiIhUk5C9s7AYbooajTM7ihyFGnERkVrm25/3kut0M/ys5jobLiIiUlf4XITteh53/AB8ke3NTiNHoUZcRKQWcRV7+Xz5Tto3j6NNk1iz44iIiEg1Cdn3JlZ3OoXNbjA7ilSAGnERkVrkq1W7KXB5GH5WC7OjiIiISHXxewnb+V880d3xxJ5pdhqpADXiIiK1RJ7TzZcrd9GtTSItGkaZHUdERESqSXDaR9hcOyhsdiPotrQaQY24iEgt8emPO/B4/IzopbPhIiIidYZhELb9KbzhrXEnDjY7jVSQGnERkVogLbuQxb/s5azODWgQH252HBEREakmQZlfYy/4g8Km14NF7V1Nob8pEZFa4MMl27FZLQzt2dzsKCIiIlKNwnbMwBfckOIGF5kdRY6BGnERkRpu5/58Vqw7wNmnNCY2MtjsOCIiIlJNbPlrcGR/j6vJVWB1mB1HjoEacRGRGswwDN79dgvhIXYGndbU7DgiIiJSjUJ3vYBhDaWo0b/NjiLHSI24iEgN9tPGdNbvzGbYWS0IC7GbHUdERESqicWdQcj+dylqMAojKM7sOHKM1IiLiNRQRW4vb3+zmSb1IujbtZHZcURERKQahe55DYu/uOSydKlx1IiLiNRQn/64g+z8Yi75vzZYrVozVEREpM7wewjZ8zLuuL74Ik4yO40cBzXiIiI1UGqmk69W7qZnhyRaJkebHUdERESqUXDax9iKU3U2vAZTIy4iUsMYhsHchZtwBNm4oG9Ls+OIiIhINQvd9QLe0Ba4E84xO4ocJzXiIiI1zM+b0lm7I5thZzUnOlxLlYiIiNQlQVnfEZS7iqImV4JF7VxNpb85EZEaxO3x8c6iLTRKDKffyZqgTUREpE7xu4nYMBVfaDNcjcaZnUZOgBpxEZEa5MuVu8jILWJ0/1bYrPovXEREpC4J3fkcducmCto8ArZQs+PICdBRnIhIDZGVV8Tny3bSrU0ibZtpvVAREZG6xOraTfi2RyhOHIw7cZDZceQEVUoj7vV6K+NlRETkCN79dgsGMFITtMkx0jgtIlLzRWy6HTBKzoZLjVehRnzQoEGkp6djGEa57xUUFHDxxReTm5tb6eFERKTExl3ZrFyfxqDTmpAQo0vRpCyN0yIitVtQxkKC0z6hsMXN+EObmh1HKoG9QhvZ7Vx22WVkZ2cTExNDp06d6NOnD7169WLKlCm0atWK6GitYysiUhW8Pj+vf7WJ+KhgBvXQ4CvlaZwWEanF/B4iNt6KNyyFwqaTzU4jleSwjXhRUREhISGlX8+fPx8o+WT9119/5dlnn+XWW2+lT58+PPTQQ1WfVESkjvp82U72ZTiZckEngoNsZseRAKFxWkSkbgjd8xL2wi3kdnkXrMFmx5FKcthGfNSoUXg8Hrp160Z2djYHDhzgwIED/PLLL3z77bd4PB6uu+463n//fdLT06lXr1515hYRqRP2ZTiZv2wHp7atR+eWCWbHkQCicVpEpPazuDMJ2/ow7vh+uBPOMTuOVKLDNuJz585l8+bN/Pzzz7Rv354BAwbg9XoZNmwYt99+O23atAEgKSmJW2+9lddee63aQouI1AV+w2DWlxsIDrIxakBrs+NIgNE4LSJS+4Vv+w8Wbx4Frf8DFovZcaQSHXaytm+//ZYtW7bQrVs3DMNg2bJlDB48mLy8PP744w/S09Nxu91s3boVq9XKzp07qzO3iEit992v+9iyJ5eR/VoRHe4wO44EGI3TIiK1m61gAyF7XqEoeTy+iLZmx5FKdtgz4ikpKXz00Uds2LABj8fDCy+8gMPh4IknnqB///68/PLL1K9fn3bt2vHcc8+VuU9NREROTF6hm3mLt9C2aSw9OyaZHUcCkMZpEZHaLWLTHRi2CJwpd5odRarAYc+If/rppzgcDvx+P3v27CErK4t69eqRnZ1NVFQU8+fPx+12s2nTJg3uIiKVbP4POyh2+7nk/1pj0aVocggap0VEaq+grO9wZH5NYfNbMBzxZseRKnDYRjwmJob4+HhiYmLYvXs3O3fuJC4ujssvv5x9+/axfft2DMOgXbt2vPXWW9WZWUSkVkvLcfHtL3s5s1MDGsSHmx1HApTGaRGRWsowCN8yHV9wQ1yNJ5qdRqrIYRvxIUOGsGjRIqKjo2nfvj1hYWGEhYUxd+5cQkNDufvuu2nWrBmTJk1i7ty5+P3+6swtIlJrffT9NmxWC+ef2dzsKBLANE6LiNROQZlfE5S7gsLmN4NNVzTVVodtxH/66SdGjBjB6aefjt1u55lnnmH27NlkZmZitVp5++23ueWWWwgNDaVbt27s2rWrOnOLiNRKO/fns3zdAc4+pTGxkVorVA5P47SISC1kGIRvnY4vpClFjcaanUaqkMUwDKOiG+/evZvGjRuza9cumjRpUvq41+vFbj/svG8nJDOzAL+/whEPKzExkvT0/EpIVHuoJuWpJmWpHuVVdU2efOdXtqfm8chVpxMWElRl+6lM+jk5yGq1EB8fYdr+zRinq1NlHROAfm7/SfUoTzUpTzUpr7Jr4kj7jOjfRpHX7nmKG11Saa9bXfQzctDRjgkOe0bc7Xbj8/nKPNa4cWOAMoM7UCsGdxERs63bkcWa7Vmce0azGtOEi3k0TouI1DKGn/CtD+INS6G4wcVmp5EqdtiR+Z133uHZZ58lKOjIB4Mej4cxY8Zw3XXXVXo4EZG6wuf38/Y3m4mPCqHfyY3MjiM1gMZpEZHaxZH2KfaCNeR1eAms+gC1tjvs3/DYsWMZO/bo9yX89NNP3HHHHRrgRUROwHe/7mNPupNJwzoQZLeZHUdqAI3TIiK1S0jqO/iCG1GcdIHZUaQaHPWjluuvv55ff/0Vm+3ggaFhGHi9Xr7//nuSkpK49957qzSkiEhtVuDy8OH32zipSQzd2iSaHUdqGI3TIiK1gK8IR+YiihqOBos+kK8LjtiI//TTT9x0001ERkaSmppKQkICUVFR+P1+CgsLAUhOTiY5OblawoqI1EYfLdlGYbGX0QNaY7FYzI4jNYjGaRGR2sGR/T0WfyHFiYPMjiLV5IiN+PPPP89//vMfoqOjGTp0KJGRkeTk5HDKKafQu3dvBg8ejMPhqK6sIiK1zp60Ar79ZS99uzYiuZ55s21LzaRxWkSkdnCkf4FhC8cTe5bZUaSaHHbWdACr1cp3333H77//TkxMDPPnz+f9999n5cqVLFiwgIEDB7Js2bLqyioiUqsYhsFb32wmLNjOsLNamB1HaqDKGqc9Hg8TJ05kxYoVAHz44YcMGjSo9D703bt3AzBz5kyGDx/OhAkTyMjIACA1NZWRI0cycuRI3nzzzdLXPJZtRUTqNMPAkf4l7vh+YAsxO41UkyM24gA//PADjz/+OLt37+bqq6/m888/Z8qUKbzwwgs88MADXH/99Xz99dfVkVVEpFZZvzOb9TuzGdqzORGhWq5Mjs+JjtNer5dJkyaRmppa+thPP/3E448/zuuvv87rr79O48aN+fnnn1m0aBHz5s3j8ssvZ8aMGQDcddddTJo0ibfffpuFCxeyb9++Y9pWRKSus+f/jq14L8UJuiy9LjlsI+71ejn11FN54IEHmDNnDsuWLaNPnz689NJLWK0lTzvzzDN5+OGH+eSTT6otsIhIbWAYBh9+v424qGD6dNVyZXLsKnOcnjZtGh06dCj9+qeffuLhhx9mxIgRTJ8+HShp+M8991xsNhs9evTg119/xefzsX79enr37o3FYqFnz56sXLnymLYVEanrHOlfYGDBnXiO2VGkGh32HvE33niDH374gWbNmpUOzmeddRYpKSlER0eXfordsmVLJk+eXD1pRURqid+3ZrJ1Xx6XDmxDkP2oFyeJlFNZ47TdbicpKan0a8MwmDJlCoMGlZyZueyyy1i5ciVOp5N27doBYLFYKCwsxOVyUb9+/dLnRkVFkZaWdkzbHov4+MqdRyExMbJSX6+mUz3KU03KU03KO+GarF4ACT1IaFQ7blPTz0jFHLYRHzhwIMHBwfzvf/9j/fr12Gw22rVrh9VqxTCM0u08Hg8ej4dPP/20WgKLiNR0fsPgwyXbSIwJoWfHBmbHkRqqqsZpi8VCv379Smfwb9OmDVu2bCEiIgKXy1W6XUFBAaGhobjd7tLHnE4nhmEc07bHIjOzAL//2J5zOImJkaSn51fKa9UGqkd5qkl5qkl5J1oTa1Eq8VmrKWh5L65aUFv9jBxktVqO+AHyYRvxpKQkRo0axahRo/jxxx957LHHMAyDF154gdjY2CoJKyJSF/y8MZ1dBwqYMKQtdpvOhsvxqapxes+ePdx2223Mnj2boqIili5dypAhQ8jLy+PLL7/k3HPPZceOHcTGxmKz2YiOjiY1NZUGDRqwdu1a+vXrR3R0dIW3FRGpyxwZXwLg1rJldc4Rly/7y6mnnsqdd95JWlqamnARkRPg9xt8vHQ7DeLDOL190tGfIFIBlTlOJycn06tXLwYPHkxwcDCjRo2iU6dO+Hw+nn32WaZPn87q1asZO3YsAOPHj+faa6+la9eurFmzhgceeICQkJAKbysiUpc50r/AF9oMX3hbs6NINbMYR7guzOPxMHbsWGbNmsXw4cP54osvGDlyJGlpaaUTwdjtdvr06cPtt99eJQEr6zI0XSZRnmpSnmpSlupR3onWZMW6A7z4yVquOr89p7atf/Qn1AD6OTnoaJehVbbqHqfdbjeLFi0iMTGRbt26lT6+detW1q5dS+/evYmOjj7mbStKl6ZXHdWjPNWkPNWkvBOqic9JwuLmuBqNw3nSo5UbzCT6GTnouC9NBwgKCiI7O5uQkBCCg4MByM7O5r333gNKZlVt2LAho0ePZuLEiSQkJFRidBGR2sUwDD5fvpMG8WF0P6me2XGkFqjucdrhcDBw4MByj6ekpJCSknLc24qI1EWOzEVY/EW4651rdhQxQYVvTvxr0haLxUJCQgKzZs3i4YcfplmzZixcuFBNuIjIUazZnsXutAIGntYE65//p4pUFo3TIiI1S3DafPz2GDwxZ5gdRUxwxDPiPp+v9M8HDhzg6aefJicnh4ceeoht27bx6aefEhkZSVRUVJUHFRGp6T5ftpPYyGDdGy6VRuO0iEgN5ffiSP+iZJI2a4Wm7ZJa5rBnxBcuXMgpp5xycEOrFY/Hg9/v5/fff2fNmjXMnTu3zEGAiIgc2ta9uWzcncM5pzTWTOlSKTROi4jUXEE5P2L15lCsy9LrrMMeDXbq1InZs2eXfp2YmMjNN99MbGwsb7/9Nq+//jrz5s3jtttuq5agIiI12efLdxIeYqdXl4ZmR5FaQuO0iEjN5Uibj2ENwR2vZRzrqsNeB1G/fn3q1z84o6/f7y/9c2pqKuHh4Tz++ONkZmayb98+GjbUwaWIyKHsy3Dyy+YMzjujGSEOXX4mlUPjtIhIDWUYBKd/VtKE28LNTiMmqdARodfrpbi4GIAGDRowceJELBYLhmHg8/nweDx8/fXXVRpURKSm+mLFThx2K/27J5sdRWopjdMiIjWHPf93bEW7cbaomuWfpWY4aiOem5vLnDlzaN++PbNnz+bss8/GbrcTHx/PSSedROPGjasjp4hIjbQnrYAf1+zn7O6NiQpzmB1HaiGN0yIiNYsjfT4GVtyJ5Zd4lLrjqI34wIEDycjIIDQ0lOzsbD755BM6dOiA3W7n4YcfJigoiIsuuojRo0cTEhJS7vkFBQXcdNNNuN1ucnNzmT59Om3btq2SNyMiEkgMw+DtRZsJC7Zz7hnNzI4jtdSJjtMiIlK9gtM+wxNzOoZDy0rWZUeduve+++6jSZMmbNq0ieuvv56rrrqKwsJCnnzySb755hsefvhh1qxZU+betL/7+OOPGTp0KK+++irXXHMNzzzzTKW/CRGRQPTb1kzW7chm6JnNiQgNMjuO1FInOk6LiEj1sRZux16wBne9IWZHEZNV6B7x8PBwXnvtNQDOO++8Mp+od+nShS5duhz2uWPGjCn9c2ZmJvXq1TvOqCIiNYfX5+fdRVuoHxdG366NzI4jtdyJjNMiIlJ9gg98BEBxohrxus5iGIZRHTvKysrikksu4eWXX9bMrSJS6326ZBszP/qDu8efxqntk8yOI1IrZGYW4PdXzmFLYmIk6en5lfJatYHqUZ5qUp5qUt4x1cQwiF12KoY9hpxTF1ZtMJPoZ+Qgq9VCfHzEYb9fLevoeDwepk6dytSpU4+5Ca+sQVc/FOWpJuWpJmWpHuVVpCYFLg9vfrmetk1jaZYYVutrqJ+Tg4426IqISN1lz/sZu3Mj+W1nmB1FAsBR7xE/UT6fj6lTp9K/f3/69+9f1bsTETHdu99uwVXs4+L+rbBYLGbHERERkQAQkjoXwxpMcf3hZkeRAFDlZ8Tff/99Fi9eTHp6Op999hkNGzbkiSeeqOrdioiYYt2OLJb+nsrgHk1pXE9nRkVERATwFxO8fx7FiUMwgmLMTiMBoMob8YsuuoiLLrqoqncjImK6Yo+POV9upF5sKEN7NjM7joiIiAQIR/oCrJ5sihuONjuKBIgqvzRdRKSu+GTpdtJyXFw68CQcQTaz44iIiEiACEmdi89RH3dcP7OjSIBQIy4iUgl27s9nwcrd9OrcgLZNY82OIyIiIgHC4s7AkfEVxQ0uAmu1zJUtNYAacRGRE+Tz+3nti/VEhgVxYd+WZscRERGRABKy/z0shpeiBrosXQ5SIy4icoIWrNzNrgMFjDm7NeEhQWbHERERkUBhGITsfR1PZGd8ke3NTiMBRI24iMgJOJBVyMdLt3Ny60S6n1TP7DgiIiISQIKyvsNesIai5MvNjiIBRo24iMhx8hsGs77YgN1mZczZrc2OIyIiIgEmdOcz+B2JFDUYaXYUCTBqxEVEjtP3v+1j4+4cRvZrSWxksNlxREREJIDYCtYTnLkQV+MrwBZidhwJMGrERUSOQ36hm/e+3cJJTWI4q1MDs+OIiIhIgAnd+SyGNRSXLkuXQ1AjLiJyHL79eS+uYh9jzm6NxWIxO46IiIgEEEvxAUJS36Go4WgMR7zZcSQAqREXETlGbo+Pb37eQ6eUeBolRpgdR0RERAJM6O6ZYHhwNbnG7CgSoNSIi4gcox/X7Ce/0MOg05qYHUVEREQCja+Q0D2v4E4cjC+8pdlpJECpERcROQY+v8GClbtolhRJ68YxZscRERGRABO6639YPVkUNptidhQJYGrERUSOwcq1+zmQ7WLgaU10b7iIiIiUYSlOI2z7ExQnDsEb08PsOBLA1IiLiByDDxdvISE6hG5tEs2OIiIiIgEmfOuDWPwunK0eMDuKBDg14iIiFbRlTy7rd2Txf6c0xmbVf58iIiJykC1/LSF7Z+NqPBFfeCuz40iA05GkiEgFGIbBh0u2ERkWxJlaN1xERET+zjCI2HQHhj2Kwha3mp1GagA14iIiFfDzpgzW78xmzDknEeKwmx1HREREAogjcyGOrG8pbHErRlCc2XGkBlAjLiJyFB6vj3cWbaZRQjgDT29mdhwREREJJH4v4ZvuwhvaAlfjiWankRpCjbiIyFF8tWo3GblFjBrQCptN/22KiIjIQSH73sDu3ICz9TSwOsyOIzWEjihFRI4gO7+Y+T/upGurBNo106VmIiIi8jeeAsK3TscTczruxHPNTiM1iG50FBE5gve/24rP72dkv5ZmRxEREZFAs/4xrO40cru8BRaL2WmkBtEZcRGRw9ib4eTHNfs5u3tj6sWGmR1HREREAoi1KBXWP05R/RF4o08xO47UMGrERUQO4/NlO3AEWRl4WhOzo4iIiEiACdv6IBhenK3uMzuK1EBqxEVEDiEtx8WKdWn06dKIyDBNvCIiIiIH2QrWE7LvdWh9Lf7QZmbHkRpIjbiIyCF8sXwnViucc6rOhouIiEhZ4VsfxLBHQvs7zY4iNZQacRGRf8jKK2Lp76mc2akhsZHBZscRERGRAGLP+4XgtE9wNbkGgrWiihwfNeIiIv/w5cpdGAYM1r3hIiIi8g9hW6bjD4rF1fQas6NIDaZGXETkb/Kcbr7/dR+nt69PQkyo2XFEREQkgNhzlhOcuZDCZjdg2KPMjiM1mBpxEZG/+WLFTjxeP4NPb2p2FBEREQkkhkH4lgfwO+rhanyF2WmkhlMjLiLyp4xcF9+s3sMZHZNoEB9udhwREREJIEFZi3FkL8XZ/CawhZkdR2o4NeIiIn/6eMl2wMKwM1uYHUVEREQCTNiOp/EFN6Qo+TKzo0gtoEZcRATYk1bAj2v2M6B7MvHRIWbHERERkQBiK9iII+tbihpfDlatqCInTo24iAgw77uthAbbGdxD94aLiIhIWaF7XsKwOHA1utTsKFJLqBEXkTpvw85sft+ayZDTmxIRGmR2HJFq5/F4mDhxIitWrAAgNTWVkSNHMnLkSN58883S7WbOnMnw4cOZMGECGRkZlbatiEggs3jzCd73FsVJwzEciWbHkVpCjbiI1GmGYfDe4q3ERgbTv1uy2XFEqp3X62XSpEmkpqaWPnbXXXcxadIk3n77bRYuXMi+ffv4+eefWbRoEfPmzePyyy9nxowZlbKtiEigC059G6svXzOlS6Wymx1ARMRMP2/KYHtqHuMGnYQjyGZ2HBFTTJs2jaeffhoAn8/H+vXr6d27NwA9e/Zk5cqV7N69m3PPPRebzUaPHj146KGHKmXbYcOGVThnfHxEpb7vxMTISn29mk71KE81Ka/O1cQwYMXLENed2JS+YLGU26TO1eQoVI+KUSMuInWWz+/ng++30iA+jJ4dk8yOI2IKu91OUtLBn3+Xy0X9+vVLv46KiiItLQ2n00m7du0AsFgsFBYWVsq2xyIzswC/3ziu9/lPiYmRpKfnV8pr1QaqR3mqSXl1sSZBWd8Rk7eevPYvUJxRUO77dbEmR6J6HGS1Wo74AbIuTReROuvHNftJzSxkRK8W2Kz671AEIDQ0FLfbXfq10+nEMAwiIiJwuVyljxcUFFTKtiIigSx090z8QXEU1/+X2VGkltGRp4jUSR6vj4+Xbqd5g0hObq2JV0T+YrPZiI6OLr1nfO3atSQnJ9OlSxeWL18OwI4dO4iNja2UbUVEApW1aA+OtM8oanQp2LS0qVQuXZouInXStz/vJSuvmPGD22I5xP1eInXZ+PHjufbaa+natStr1qzhgQceICQkhGeffZbp06ezevVqxo4dWynbiogEqpA9rwIGruTxZkeRWshiBPh1YZV1P5juVyhPNSlPNSmrttbDVezl1v8to0n9CG66uOsxPbe21uREqCYHHe1+sJpk69atrF27lt69exMdHQ2A2+1m0aJFJCYm0q1bt0rbtqJ0j3jVUT3KU03Kq1M18RcTv6Qdnqju5HV957Cb1amaVIDqcdDRjgl0RlxE6pxPfthOgcvDv3qnmB1FJGClpKSQklL234jD4WDgwIGVvq2ISKAJPvAxVnc6rsYTzY4itZTuEReROmV3WgELV+2hV+cGNG8QZXYcERERCUChu1/CG9oCT3w/s6NILaVGXETqDL9h8PqCjYSF2LmgT0uz44iIiEgAsuX/TlDuCooaXw4WtUtSNfSTJSJ1xg+/p7Jlby4X9k0hIjTI7DgiIiISgEJ3v4xhDaWo4Rizo0gtpkZcROqEApeH9xZvpVVyND07NjA7joiIiAQgiyeHkNR3KWpwEUZQrNlxpBZTIy4idcK8xVsoLPIy9v/aYNVyZSIiInIIIfvewOIvpCj5crOjSC2nRlxEar1Nu3P4/rdU/u/UxiTXqx1LS4mIiEgl87sJ3fks7tiz8EZ1NjuN1HJqxEWkVvN4/cz+cgMJ0SGc37O52XFEREQkQAXvfw9b8T4Km11vdhSpA9SIi0it9sXynaRmFnLJ/7Uh2GEzO46IiIgEIsNP2I4ZeCM64IkfYHYaqQPUiItIrZWa6WT+sh2c2rYenVLizY4jIiIiAcqRsQC7cwOFzaaA5pKRaqBGXERqJePPNcMddhuj+rcyO46IiIgEsLAdT+ELaUJx/X+ZHUXqCDXiIlIr/bhmPxt25XBB3xSiI4LNjiMiIiIByp6znKCc5RQ2vRasdrPjSB2hRlxEap0Cl4d3v91CSqMoenVuaHYcERERCWBhO57GHxRHUaOxZkeROkSNuIjUOh98txWnS2uGi4iIyJFZXTtxpH+BK3kC2MLNjiN1iBpxEalVtu7L5btf9zGgezJN6keaHUdEREQCWMje2YCFouTLzI4idYwacRGpNXx+P68v2Eh0hIPzz9Sa4SIiInIEfjehe+fgThyIPyTZ7DRSx6gRF5Fa49uf97LrQAGjBrQmNFiTrYiIiMjhOdI/w+pOo6iRzoZL9VMjLiK1QoHLw8dLt9O+WSzd2ySaHUdEREQCXOieV/GFNMGdMMDsKFIHqREXkVrhkx+2U1jsZWS/Vlg0QZuIiIgcgc25GUfWdxQljwOLzew4UgepEReRGm9/ViHf/ryXXp0bklwvwuw4IiIiEuBC9ryGYbHjaqgly8QcasRFpMZ779stBNmtDDurhdlRREREJND5ighJfZPieudhBNc3O43UUWrERaRGW78zm182ZzDk9KZEhzvMjiMiIiIBLmTf61g92ZqkTUylRlxEaiy/3+CdbzYTHxXC/53S2Ow4IiIiEuAsnhzCtz6IO/ZMPHG9zY4jdZgacRGpsZat3c+utAL+1acFQXZNtCIiIiJHFrbtESyebAraPAya3FVMpEZcRGokj9fHh0u20SwpklPb6v4uEREROTKbczOhu1+kqNGl+CI7mR1H6jg14iJSI329eg9ZecVc2LclVn2iLSIiIkcRvukODGsozpS7zI4iokZcRGqeApeH+T/upFNKPG2bxpodR0RERAJcUMbXBGcsoLDFLRjB9cyOI1I9jbjH42HixImsWLGiOnYnIrXc/B93UOT2ckGfFLOjiIiISKDzFRGx8VZ8oc1xNbnK7DQiANiregder5dJkyaRmppa1bsSkTogI8fFop/30LNjA5ITI8yOIyIiIgEubPvj2As3k9P1A7AGmx1HBKimM+LTpk2jQ4cO1bErEanl3v9+GxaLhWFnNjc7ioiIiAQ4W8E6wnY8RVGDkXgSBpgdR6RUlZ8Rt9vtJCUlHffz4+Mr74xXYmJkpb1WbaGalKealBVI9di0K5sV6w5w0YDWtElJNC1HINUkUKgmIiIScAwfkesmY9gjKWj9H7PTiJRR5Y34icrMLMDvN074dRITI0lPz6+ERLWHalKealJWINXDMAxeeP83osId9O6YZFquQKpJoFBNDrJaLZX6AbKIiBy/kN0vE5S7irwOMzEcCWbHESlDs6aLSI2wemM6W/bkMvys5oQGB/xniCIiImIia1Eq4Vvuxx3fn+KkkWbHESlHjbiIBDyP1897i7fQKDGcszo1NDuOiIiIBLiw7Y9i8ReTf9ITYLGYHUeknGo7rfTwww9X165EpJZZ9PMe0nOKuPGizlitGkxFRETk8KyF2wnZO5uiRpfiD2thdhyRQ9IZcREJaHlON5/+sIMOzePo0CLe7DgiIiIS4MK3PQwWO4XNbzE7ishhqREXkYA2b/FWij0+Rg1oZXYUERERCXC2go0Ep76Dq/EV+EMamB1H5LDUiItIwNqyN5elf6Tyf6c2pkF8uNlxREREJMCFb30QwxZOYbMbzI4ickRqxEUkIPn9Bm8s2EhsZDDnndHM7DgiIiIS4Ox5vxGc9hGuppMwHLqdTQKbGnERCUjf/rKXXWkFXNy/FSEOLVcmIiIiRxa2dRp+ewyuJteaHUXkqNSIi0jAyXO6+eD7bbRrFkv3NolmxxEREZEAZ89eRnDGVxQ2vxEjKNrsOCJHpUZcRALOe4u34Pb4GHN2ayxa+1NERESOxDCI2HIfPkd9XI2vMDuNSIWoEReRgLJ5Tw4//LGfc05tognaRERE5KiCMr8mKGcZhS1uAVuY2XFEKkSNuIgEDJ/fzxtfbSIuShO0iYiISAUYfsK3PIAvtBlFjS41O41IhakRF5GAsfiXfexOK+Difq0IdtjMjiMiIiIBzpH2CUH5v+FscTtYHWbHEakwNeIiEhBy/5ygrX2zWLppgjYRERE5Gr+X8K3T8YafRHGDi8xOI3JMtCaQiASEdxdtxu3xMVoTtImIiEgFBO9/B7tzE7md3gCLrqSTmkVnxEXEdCvXH2DZ2gMMOb2pJmgTERGRo/O7Cd/6MJ7IrrjrnWd2GpFjpjPiImKq9BwXs7/cQEqjKM7VBG0iAWPw4MHEx8cDcMopp3DhhRdy/fXXAzB06FDGjBkDwMyZM/niiy+Ii4vjkUceISEhgdTU1ApvKyJyPEL2zsZWtJP8tk+CrqSTGkiNuIiYxuvzM/OTtQBceV577DZdpCMSCPbv30+TJk343//+V/rYhAkTmDRpEr169eKyyy6jb9++7N+/n0WLFjFv3jxWrlzJjBkzmDZtGnfddVeFtxUROWa+QsK2PYYn5nQ88QPMTiNyXNSIi4hpPvlhO1v35XHV+e1JiAk1O46I/GnVqlVs3LiRUaNGYRgGt99+O+vXr6d3794A9OzZk5UrV7J7927OPfdcbDYbPXr04KGHHsLn81V422MVHx9Rqe8zMTGyUl+vplM9ylNNyguImqyfCe792Hq9Q2K9KLPTBEZNAojqUTFqxEXEFOt3ZPHZjzs5s1MDTm1b3+w4IvI3LVu25OWXXyYlJYVVq1bx+OOPU7/+wX+nUVFRpKWl4XQ6adeuHQAWi4XCwkJcLleFtz1WmZkF+P3GCb67EomJkaSn51fKa9UGqkd5qkl5gVATizefuDX/wRvfj1xLVzA5TyDUJJCoHgdZrZYjfoCsRlxEql12fjEvfrKWpPgwRg9oZXYcEfmH5s2bExISAkDbtm3ZvHlz6f3iAE6nE8MwiIiIwOVylT5eUFBAaGgobre7QtuKiByr0J3PYPVk4ky52+woIidEN2SKSLXy+f28+Mlaijw+Jg3vSIhDnweKBJr777+fH374AYAvv/ySTp06ER0dTWpqKgBr164lOTmZLl26sHz5cgB27NhBbGwsNputwtuKiBwLq2snYTuepqj+CLzR3cyOI3JCdAQsItXqw++3s2l3DhPPbUejBC1VJhKIrr32WqZOncrDDz9M/fr1uf/++1m/fj3XXnstXbt2Zc2aNTzwwAOEhITw7LPPMn36dFavXs3YsWMBGD9+fIW3FRGpqIhNdwBWnK0fNDuKyAlTIy4i1ea3LRl8vnwnvTo35PQOSWbHEZHDaNSoEW+//Xa5x5o3b87atWuZPHky4eElH6TNmTOHRYsWMWjQILp1KzlDNWDAgApvKyJSEUGZiwhO+xRny3vwhzQyO47ICVMjLiLVIr/QzWufr6dxvQjdFy5SQ6WkpJCSklLmMYfDwcCBA09oWxGRI/K7idh4C77Q5hQ2nWx2GpFKoXvERaRavLlwE84iLxPPbYcjyGZ2HBEREakhQne9iN25iYI2D4M12Ow4IpVCjbiIVLlVG9JYuT6NoWc2J7le5a4DLCIiIrWX1bWLsG3/oTjh/3AnDjI7jkilUSMuIlUqz+nm9QUbaZoUyeAeTcyOIyIiIjWF4Sdy3TUAFJz0hMlhRCqX7hEXkSpjGAZvfLWRIreXCUPaYrPqsz8RERGpmJDdL+HI+o78tv/FH9rU7DgilUpHxSJSZZavPcBPG9M5/8zmJCfqknQRERGpGGvhViI234s7fgBFjS41O45IpVMjLiJVIj3HxetfbaRVcjSDTtOn2CIiIlJBho+otVdjWB3kt3sWLBazE4lUOl2aLiKVzuf389L8dVgsMPHcdlitGkBFRESkYsK2P0FQznLy2r+IP6Sh2XFEqoTOiItIpft82U627Mnlkv9rQ0JMqNlxREREpIZwZHxF2NYHKUq6iOIGF5sdR6TKqBEXkUq1bV8eHy/dwWnt6nN6+ySz44iIiEgNYS3cSuQfl+ON7Eh+u//qknSp1dSIi0ilcRV7efGTNcRGOhj7f63NjiMiIiI1hbeA6N/GgMVCXuc3wBZmdiKRKqVGXEQqhWEYvL5gIxm5RVwxtD1hIUFmRxIREZGawPARtXYStoIN5HV8FX9oM7MTiVQ5NeIiUil+XLOf5esOcP6ZzWmVHGN2HBEREakJDIOI9VMJTvsIZ6tpeOL7m51IpFqoEReRE7Y/q5A3vtpEm8YxnHt6M7PjiIiISA0RvuUBQve+SmGzG3A1m2x2HJFqo0ZcRE5IsdvH/z5eg91mYeJ5WqpMREREKiZ0xwzCdjyBq9FlOFveZ3YckWqlRlxEjpvX5+e5j/5gd1oBl5/bjrioELMjiYiISA0QvO9NIjbfTVH9ERS0fVIzpEudo0ZcRI6L3zB47fP1rNmWxaUDT6JzywSzI4mIiEgNEJS5iMh1k3HH9SW/w0yw2MyOJFLt1IiLyDEzDIN3F21h2doDDO/Vgl6dG5odSURERGoAW/4aon4biy+8DXmd5oDVYXYkEVOoEReRY2IYBh98v42vVu2mf7dkzj29qdmRREREpAawFu0l+pcLMOyR5HadhxEUbXYkEdPYzQ4gIjWH1+dn1hcb+HHNfnp1bsioAa2w6J4uEREROQqLJ5voX/6FxZtPzikL8Ic0MjuSiKnUiItIhbiKvTz/4R+s3ZHNsDObc17PZmrCRURE5Oh8hUT/chE25xZyT34fX2QHsxOJmE6NuIgcVW5BMU+99xt70pxcNvgkzuqke8JFRESkAvweon4biz13FXmdZuOJ6212IpGAoEZcRI5of1YhT77zK/mFHq67oBOdUuLNjiQiIiI1geEjcu3VBGcuJL/tf3HXP9/sRCIBQ424iBzWxp1ZPPT6aiwWuGV0V5o3iDI7koiIiNQEfg+Ra68kZP88nC3voSh5nNmJRAKKGnEROaQV6w4w68sNRIUFcePILtSPDTM7koiIiNQEPhdRv/+b4IwFFLS8H1fzG8xOJBJw1IiLSBnFbh9vfr2Jpb+n0rZZHFec147ocK3xKSIiIkdn8eYR9ctIgnJ+JL/t0xQljzc7kkhAUiMuIqV2pxXwv4/XsD+zkHPPaMrlwzqRleU0O5ZIpTE8xfidmRgFWfgLMjEKMrElNMferKvZ0UREajxr8X6ifrkAe8E68ju+QnHSBWZHEglYasRFBMMw+Hr1Ht77divhIXamXtyFds3isNmsZkcTqTDD8GO48jDyM/DnZ5Q22v6CLAxnye8U/+ODJYuFoDa91YiLiJwgW8FGon8ZgcWTTW6Xd/AknG12JJGApkZcpI7Lc7p59fP1/L41k04p8Ywf3JYoXYouAcLweTAKczCcOfgLczBcuRhFBSW/iv/8vSgfw5WPUZQHPm/ZF3CEYY2IxxIRR1D9Vlgi4rCGx2GJiC95PDwGi1VDoYjIiQjK/pGoXy/GsAaT2/1zvFFdzI4kEvB09CFSh63emM7rX22ksMjLmLNb0+/kRlgsFrNjSR1i+L0YeRn489LwF2Tgz0vHKPjzbHZ+JoYr99BPdIRiCY7AEhKJJSwGa1wylpAorJHxWCMTsEQklDTajtDqfUMiInWJ4Sd05zOEb3kAX2gzck/+AH9oU7NTidQIasRF6qA8p5s3Fm7ipw1pNKkXwU0XdyE5McLsWFLLGIYBniIMVy5+V17Jme2/LhvPz8CfewAjPx0M/8EnWe1YIuOxRiRga9IZy59nra1hsVjCokt+hUToLLaIiMmsRfuIXHsVjqzFFNcbSn67ZzCCYs2OJVJj6EhGpA4xDINla/fz9jdbKHJ7Gd6rBYNOa4Jd94LLMTK87tIJz/L2Oinev6/kfmxn9p+XiZf8wucp/+Tg8JJGO6EJ1hanYI1JwhJVD2tkYkmjbdHPo4hIwDIMgve/R8TGm7H4islv9yxFDceCrqgTOSZqxEXqiH0ZTt74aiMbduWQ0jCKcYPb0igh3OxYEqAMwyg5k52Tij9nP/68tJJLxvMzMPIzSprsP7n+/N0SGo0lIg5LWDTW+GQsIZFYQ6NKHg+NKrmEPDIei0Nr0ouI1Ei564lefRWO7O/xRHUjv8NMfOGtzE4lUiOpERep5bLyivhm9R6+WrWb4CAb/z6nDb26NMSqT67rNMPnKTlz7cr9cyK07JIz3HkH8Oem4c87AJ6ig0+w2rFEJmCNTMAW37Rk0rOIeCwR8SQ0aUx2sQOLLci8NyQiIlXG4sklbMeTsOtZ7NZw8k96iqLkcWCxmR1NpMZSIy5SC3l9fv7Ymsn3v+3j922ZGAac0SGJi/q21IzodYRRVPBnU30Af176n2ey0/E7szBceWWb7L9YbFiiErBG1ScoqSXW6AZYY5KwRieVnOk+zCXjQbGRWNLzD/k9ERGpwfxuQve8Qti2R7B6sqDFOLIa343hSDQ7mUiNp0ZcpJbw+w027c5h5foD/LQxnQKXh+gIB0NOb8qZnRpSL0azR9c2hmFgFObgz91fcgl51h782XvxZ+3FKC4os60lLAZLZAK2xBZYQiNLZhsPjcIaGl2yhFdYTMnl41ad3RARqfMMA0faR0Rsvg+bazvuuD44Wz1AbMpZGPrgVaRSqBEXqcE8Xj8bdmXz86Z0ftmcQZ7TTXCQja6tEji1XX06tojDZtXEVzWZ4ff/efl4VsmZ7ZzUkl+5qfhzD4DXfXDjoFCscY2wN+9WciY7KglLdP2S5bzsuhJCRESOLij7B8I33UVQ3mq8Ee3J6fo+nvgBmoxNpJKpERepQfx+g91pBWzYlc3GXTls2JVNkdtHsMNGxxbxdG+TSOeUBIIdOqtZUxh+X8m92fnpJROi5f+5hnZBZsnvzhwwfAefYLFgiUzEGp1EUIO2WKPrl/yKaYAlPE7rwIuIyLEzDIJylhG6/QmCMxfiC25IXvsXKG5wse4DF6kiasRFApjfMNiemsemXTls3J3Dlj25FBZ7AagfF0aPdvXp0iqBtk1jCbJroKwJ/IW5+NO24TuwGV/aVnxp28H3t7PaFhuWiFisEfHYklr/OSFaHNbwuJL1taPq6+y2iIhUDr8XR8ZXhO14iqDcFfiD4iloeS+uJleDTStciFQlNeIiASgtu5Af1+znxzX7ycgtmVSrQXwYp7StR+vGMZzUJJbYyGCTU8rR+F15+DN348/ahS99B760rRj5GSXftNqwxjclqG1vrHHJWP9aRzs8DotuJxARkapiGNgK/iBk39sE738Pm/sAvpAm5Ld5jKJGY9WAi1QTNeIi1cwwDPKcbg5ku0jNdJKaWcj+rEKcRR6Kin0UFnvJzi/GArRrHsfwXi1o3yxOs50HIMMwwF2I/6/LyfPSStbc/nPyNMOVW7qtJTwOW70W2Nr3x1ovBVtCM53ZFhGR6uEtwJG9BEfm1wRlfIPdtQ3DEoQ74RwKGlyMO3EQWLUEpUh1UiMuUgkMw6DI7SOv0E2+00NeofvPP7vJK/SQX+gmv9BDVn4xmblFeH3+0uc67Fbqx4URGRZETHgwIcE2GsSH06NdfeKiQkx8VwJguF3489JK79/O8OZSmJ6KkZ+JvyCj/DJgweFYYxpga9wRW1wy1vgmJWe8Q6PMeQMiIlL3+IoIyltNUNZiHFnfYc/9CYvhxbCG4Y47C1fTaymuPxzDEW92UpE6S424yJ/8fgOny0NWXhGFxV6K3D6K3F6Kin0UuX24ir24ir0UFntxujzkFZY03PmFbvKcnjLN9d+FBtuIDHUQGRZE43oRdG2ZQHx0CIkxITSIDyc+OgSrJtgylWEYGEX5JUt/5aT+7fd9GIU5Zbb1hIRDeDzWqERsjdr+eQ93fMnvUYlYQyLNeRMiIlI3GQZW13aC8n7GnruKoJyV2PN/x2J4MLDijeqCq+lk3PH98MT0AKtubRMJBGrExXR+w8Dj9eP2+HB7/PgNo6QxMkq+5/cb+I2SRtnr9+P3G/h8Bj6/gdfnL/3d4/X/7XcDj9eHx+fH7Sl57eI/fy9y/9Vkl/wq9pT88ngP3Uj/U3CQjfBQO1FhDqLDHSQnhBMV7iAyrKTZjv7zz1HhDiJCgwiy637fQGH4vfhz00oa7ex9JZeQ5x7An3cAip0HNwwKKTmrndwea0zDkvu3o+phjUygXnIS6VpDVUREqpthYC3ahb1gAzbXNqyF27EXbsKe9wtWT3bJJtZQPFEn42p6LZ7oU/HE9sQIijE3t4gckhpxOSyf309GbhE5+cWlDWtp8/pXM+vx4f7z9+I/t3F7/BR7fHh9fgzjYBNdpqn+68/+koa6qlgsEGS3EhxkIzjIhiPIRojDRqjDRkxEMMEO29++ZyUxPhyfx0eIw0aIw/7n7zZCgu2EBZd8bbepsQ5Uhtv155JfWfid2RjOnIPLgBVk4s9PB//BpcAs4XFYY5IIanFqybrbsY2wxjTEEh6rZcBERKR6+YuxFu3FVrQHiycLi8+FxV+ExZNFUO5qgnJXYnWnlW5u2MLxhqVQXG8o3qiTS35FtNO93iI1hBpxKeUq9vLblgxWb0pnT1oBGblFR2yS7TYrIX81sg4bwUElDW90hIPgoJKG1WoBi9WC1WLB9ufvFisHv7aW/O4IsuGwW3EE2bBZLVgsYLGU/P7351qtFmw2CzarFZvVgt1mxW4r+X6Q3Vrytd2Kw24lyG7FdoyzTycmRupsZwAzfJ6Se7Pz0/Hnp5esuZ2fjj8/AyMv/f/bu9fYKMq2D+D/e2b20NIDtLSUwmNQnkRBHgIvvq8gIY1ijCBRwQMCIgnUEx+M0EggnkDQKEKioJBwUlG/iNbEJ6BBIEbCWdAgB/Wxvv2A4otQqu0eZ2fu98PMbrfdA63t7gzt/xc2uztz78zdi52d69q5ZxYy0pryGlFQag0dL/8HtGH/ZRXbZUOs393WODyPiIh6mBkDzDCEGYEwWqHoTRDRS1D0SxB6M5TYXxCxPyH0JqiR36FE/g9K9Hco0T8yLtIouBbR8luhl/4PYsX/glFwHaS3wjriQERXJRbivZAeMxAIxxAIxxAKx+zzna3zmyPxo9e6Aa/Xg0AgAimBppYwvv+lCTHDRP8iL/45pBQ33VCJygEFKC/xo8CnJY4c+31qotAm6inSjEEG/4IMNsMMNkMGLltHtlsvQbY2WcV24DKApC+HFM36be3iCqjXDYMoroBSXG4d6e7XH6KwP4TKIwNERL2KNCGMAGCErGLXjABSB6QOYUYBGYMwYwCMtscyZs/Xk+6jEFK3RkpJHULGADNiLcMMA/8LFAUDEGbUnhaBMMOAGbWOVJsRe1oEwgzZ/QlBSOPKf4Lih+kZANNbBcM/FHrpTTD91TD8Q2H6h8L0DIRU/YDih1SLID2luY8rEeUVC3EXixkmWuwrbreGdKu4DukIhHW0BHW0hqz7QFhHMBxLXEws2olznTVVwKOpAABFAAU+DTVjqvHfN1Tin0NLefEw6hZpxiAjQchwK2Sk1boPtyTuEQlARgLW89BfMO1pKYQKUVQGpagMavUIKMUDoZRUQhQPtH9zuz+E4BdCRESOkUZSQRyCMMKJglUYIQijFcIIQBhBwAja04N223gxnfTrE1JCGC1Q9MsQerP1OkjrJk0IIwjFyN3INQkBKD5IxQ9oXnjhBYQHUvFa0xQvpOKD9PSHqfghFZ/dvsAunAsg1QLI+Dy1EKanHKa3HNJTBtPTH1Ir4QXTiCg/hfimTZvw+eefo6ysDK+99hoGDhyYj9W6jmlKhKMxXG6NorklgubWCP4KRhG0C+zW+JW4A1H8FYgiGIllXJbXo6C4wIuiQg+K/BrKSvwotM9jLvRr6OfXUOj3oNCvocBn3+zznn1ea8g2h2ETYP8WtqEDsShkLGrdG9Z9qFVF7FIzpB6x50cg9TCgRyD1kFVsR0NANAgZDdrPg6k/6ZVM9UD4iyB8/SB8RVDKr4FaUAzhL4EoKLGPZA+AKCy1hpV38fQCInI35gR5JA1riLQRThTG1pDpeMEcnx4GWhT4WuzPbimhxJqhRC9B6BetojjW0lZUx1rbHpuRv9c1xQepFkIqhVbBmnQAQKolkJ7+MPz/gFQLAQjrJhTrNVoxpFoMqRbYRbAXEF77XoNUPIDQrMdCBYRqFdPCAyjJ915A0ex5WuI18b5UVBSjiXkSEeVIzgvxEydOYN++ffj4449x9OhRvPnmm1i5cmWuV9uOYZj45cf/4M/mYGKalICEdWVuKSWkmXQBMVjPDVMmLjJmmCZiMWkN7Y7GENaN9vMN+6rfMRO6biBqmNB16yre0ZiBqG5Aj0mIpGG18X2OIoBCu1guK/DgmjIN/ao1FPkL0c+voZ/fg0K/igK/hgKvVVC3vxK3TPNQt27SnmACCEsgbP3xBoBwpBBGPCZS2i+VdnBk+wXKDvft1pk0TcbnyQ7NpP0v3Tnn9utkpiP5yctNXl+HNin9Sdcsy2ulRMtvfugt4ZT2Mtvy08YqTZ8T0617GZ8mzfTtTQOQJqRptL3WNK320oA0pZ1kmYl7ab8GppG4ycTjGKQZAwzrJg3dKsANPWO4gplmCBXw+iG8hRDeAghvAZSSSsBbaE3zFUL4iqyC218E4S9ue8zzson6LDfkBAAgoheBlgtQgq2If+6K+OcsgLbPahPJn9uAhLA/gwHrXsj4sOfkfZi059mfyfEh0tKw2xqJ4dJCRgDDPppsRq1hzzJqnWecGDqt2/f2EOn4EGxTt9tGkoZO249lpFNDpJOVdHguhQbTYx3JlVoRpFoE0zcYUu1n3bQi+0hwgX202A+p+u3HdqGt9rOGVqsF1mPFD6gF1n6EiKgPy3khfuDAAUybNg2qqmL8+PF45ZVXcr3KFKf+/QGuu7APjpxdo9q3ztYeIfuWhV1id1vGIqsPy3IcN/+ECigKIOI3AQgBoaht0xQFUFRreLawHsdvQiiAqkF4fNZzRQNUD6CqEIoH0DzW+dOqB9C8EJrXOlqteRPP+5f3x58BA9B8EJoXwuMHPD6ed01Ef4sbcgIR/QPlX98ASB3leV97dtaRWm+HI7bJR3B9iedS7Qfp8Sa199rDpL1tw6KFN6kw9ttHoQvsgtkePm0/LxtYhktNbacISa0EUuvPi4EREeVIzgvxQCCAkSNHArCugh0Mdq38Ky8v6nYfxt8/C/85NBTJR02FEBCANdJJCAghrCt8i7arc8ev2K3Er/qtCni91kXLFCV1x2QvMf4kZW7SytM/TmnXcbnplh9vlGZHaU8TEJ1vL4TdvmMf48sQST/rJJLuOi5XJK0/3XJSVm6tO+0Ov23dKf1u91ykn9xhXrZYpXROJMU/XZsMsUj8HYm/OfnvS37eVmB3XJZbfj6rwOkOuFBFRbHTXXAdxoQ6ww05AWQRUPNvIHIhaWLHfVfyZ7XS4bkKIP7lpz0EWtGsacniQ6KVtqHR1jBote01QrXOFVb9gOpPXPPCqU//nghvb8PPtlSMSSrGpD3Go3NyXogXFRUhFGo7xNvamvrzQtlcutQKs9u/M+3B2Kn39Nj50NEeWYrzrspzxDu+FXr4J8hzF5PkIY9Xj6vyPZJjjEkqxqSNooieKRZ7KXfkBAA8t6CiOg/vWwmgU6PDDQBpLliZR9yOUzEmqRiTVIxJe4xHmyvlBDm/CtKYMWNw+PBhAEBjYyMGDBiQ61USERGRCzEnICIisuS8EJ8wYQJ+/vlnrFq1CosWLcLcuXNzvUoiIiJyIeYERERElpwPTVdVFdu3b8e+ffswZcoUjBs3LterJCIiIhdiTkBERGTJy++Ie71e3HnnnflYFREREbkYcwIiIqI8DE0nIiIiIiIiojYsxImIiIiIiIjyiIU4ERERERERUR6xECciIiIiIiLKIxbiRERERERERHnEQpyIiIiIiIgoj1iIExEREREREeURC3EiIiIiIiKiPGIhTkRERERERJRHLMSJiIiIiIiI8khzugNXoijClcvqLRiTVIxJe4xHKsYkFWNiYRxyq6fjy/+v9hiPVIxJKsYkFWPSHuNhuVIchJRS5qkvRERERERERH0eh6YTERERERER5RELcSIiIiIiIqI8YiFORERERERElEcsxImIiIiIiIjyiIU4ERERERERUR6xECciIiIiIiLKIxbiRERERERERHnEQpyIiIiIiIgoj1iIE11BIBDA/v37cebMGae74hqMCRER9VXcB6ZiTIi6ztFCvLW1FU888QTmz5+P++67D2fPnsX58+cxc+ZMzJw5Ex9++GHGdgDSts0kU1td1/Hoo4/iyJEjXe4rAOzatQsPPPAAZs+ejVWrVkFK2Z2QOB6TTMvNJFP8Dh06hAULFvzdMCQ4HY9oNIra2lqcOnUKa9aswbvvvpt1GZs2bcL06dOxYMECXLx4EUDve490JSZ9ZbtJtmjRItTX12ddRq63G8AdMZk6dSrmzp2LuXPnYt26dVmXkW7bAXo2JuRubnjPMidwd06QqU9u3QcCzAs609eejonT8UjGnIA5QVbSQR988IHcuXOnlFLKvXv3yieffFLOnz9ffvXVV9I0TTlv3jz566+/pm0npUzbNpN0bXVdl7W1tfKuu+6Shw8f7nJfw+Fw4l5KKadPny7PnDlzVcck03LTyRS/3bt3y0ceeUQ+/PDD3YqFG+Jx5swZ+eWXX0oppfzhhx/k/PnzM77++PHjcubMmTIWi8mDBw/K5557rle+R7oSk76y3cTt3LlTjh49Wn7yyScZX5+P7UZK52Ny/vx5+fjjj3eqr+m2HSl7Pibkbk6/Z5kTuD8nkNL5mDAvcH9e4HQ84pgTMCe4EkePiM+ZMwdTp04FAFy6dAkVFRU4e/YsampqIITAxIkTcfTo0ZR2lZWVMAwjbdt0srVduXIlRo0a1eW+VlZWwufzYcOGDfD5fIjFYmhpaUF5eflVHZN0y80mXfxGjBiBl19+uVtxiHM6HiNGjMDtt9+OhoYGbNiwAffee2/Gvh44cADTpk2DqqoYP348vvvuu175HulKTPrKdgMAf/zxB7Zu3YpZs2Zdsb+53m4A52Ny7Ngx/Pjjj5g1axYeeughfP/99xn7mm7byUVMyN2cfs8CzAncnhMAzseEeYH78wKn4wEwJ2BO0Dma0x0AgKamJrzzzjvYvHkzTp48mZheUlKCCxcupLTbsmULQqEQBg0alNJ2+fLlaGhoaLf81atXp22raRqqqqratT158iRef/31dtNqampQW1ub0odkW7duxa233nrFnVRnORWTdMvNFpOO8QOAoUOH4ty5c3//j0/D6XgcOXIEjY2NGDBgQMZ4BAIBjBw5EgAghEAwGGzXpre9RzoTk7603bz44otYtmwZDh06BCD7Z0m+thvAuZhMmjQJW7ZswfDhw3Hs2DGsXr0azzzzTJe2nVzFhNyNOUEqpz/v3ZYTJPfJzftA5gXObjvMCVIxJ3AXxwtxXddRV1eHuro6VFVVIRqNJuYFAoHEOSLJ7aqrq2EYRtq2y5cvT1lHprbpjB49Gu+///4V+1pdXZ2Yvn//fuzfvx/btm3r0t+eidMx6bjc6urqjDHJB6fjAQCzZ8/GLbfcgsWLF6O+vj5tPN566y2EQqHE89bW1sTj3vYeAToXk3R9iOtNMdmxYweGDx+Om266KbHTzfZZki9OxuTaa6+F3+8HYH2L3dDQkDEm2bYd6lvc8NmWjDmB+3KCjn1y8z6QeYFz2w5zglTMCdzH0aHphmGgrq4OkydPxuTJk6GqKkpLS3H+/HkAwOnTpzF06NCUdgAytk2nK20729e4EydO4I033sD69evh9Xq7HIMrrSffMcn0dzrF6Xjs2LEDa9asAQA0NzejtLQ0Y1/HjBmDw4cPA0DiG2Gg971HuhKTvrLd7NmzB9988w3mzp2LTz/9FJs2bcLevXu7/Xd1h9MxWbFiBQ4cOAAA+OKLL7IO98207VDf4vR7tjt9jettn21uywkA52PCvMD9eYHT8WBOwJygs4TM9DVwHnz00UdYtWoVbrzxRgBAdXU1pkyZgo0bN2Ls2LHYv38/6uvrsXPnzpR2a9euxZ49e1La9uvXL+26srVdunQppk+fjptvvrlLfV27di0mTZqEwsJClJWVAQCeeuopTJgw4aqNSablZpMufufOncOyZcu6/e2f0/HweDxYsmQJfv/9d/h8PrzwwgsYPnx42tcbhoE5c+Zg1KhROH78OO6//37MmTOn171HuhKTvrLdJLddv349hgwZghkzZmTtcy63G8D5mDQ3N6Ourg6BQACDBg3CihUrMGTIkLSvz7Tt9HRMyN2cfs8yJ3B/TuCGmDAvcH9e4HQ8mBMwJ+gsRwvxTBoaGnD69GnU1NRk/VYtl23dhjFpz63xiEaj2LdvHyoqKjBu3Lguv7473BoTJzEmqdwaEye3HXI3t75nncSYpHJrTJgXuAfjkcqtMekrOYErC3EiIiIiIiKi3srRc8SJiIiIiIiI+hoW4kRERERERER5xEKciIiIiIiIKI9YiBP1Ub/99hvGjx+feD5x4kT89NNPDvaIiIiInMCcgCj/NKc7QEQ9Z+PGjdi4cSOKi4tT5um6Dk3TcPDgQQCAx+OBx+NJzPd4PNA0DceOHUNtbW3ip0dOnz6Nb7/9FoWFhfn5I4iIiKjbmBMQuRsLcaJeRFVVTJ06Fa+++mrKvCNHjqCurg6A9c33Y489hsuXLyd+2/LixYtYuHAhnn/+eQwbNgz19fUAgOuvv77dzpmIiIjcjzkBkbtxaDpRL6KqaqfmDx48GNu2bUNZWRnq6+tRX1+PyspKvP3222m/ORdC5KS/RERElBvMCYjcjUfEiXoRRcn+3Vp8vhACmqahqakp8e33hQsXoGkahBBobGxMTCciIqKrD3MCIndjIU7UiwghsHv3bhw/fjxlXjgcThlOFv/2GwBuu+02AICUMmUYGhEREV1dmBMQuRsLcaJe5o477sh4PtiyZcvaTev47TcAGIaR+04SERFRzjEnIHIvFuJEfVAsFoNpminffuu6jlAohMbGRtxzzz0O95KIiIhyjTkBkTNYiBP1IlLKTs3ftWsX3nvvPVRVVeHBBx8EYA1JW7x4MWbMmIGamhqsW7cOAIehERERXY2YExC5G6+aTtSLXGmna5omAODuu+/Gxo0bUVVVhe3bt2PJkiWoqanBZ599hhMnTmDs2LH56C4RERHlCHMCInfjEXGiXiQWi2W9MEt8pxyLxbB06VLU1NTA7/dj1KhReOmll+Dz+fD111/j2WefzXfXiYiIqAcxJyByNxbiRL2IaZpZL8zy9NNPAwBOnTqF4cOHY968eQAAv9+PzZs3o7a2FvPmzcOgQYMAAAsXLsTgwYOhafyoICIiupowJyByNyGvNG6FiPqMX375Bddcc01iJ3vixAkMGzYMZWVlDveMiIiI8ok5AVFusRAnIiIiIiIiyiNerI2IiIiIiIgoj1iIExEREREREeURC3EiIiIiIiKiPGIhTkRERERERJRHLMSJiIiIiIiI8uj/AQmcxc6p8YxCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1224x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confirmed_country = train.groupby(['Country/Region', 'Province/State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country = train.groupby(['Country/Region', 'Province/State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date = train.groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date = train.groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date = confirmed_total_date.join(fatalities_total_date)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17,7))\n",
    "total_date.plot(ax=ax1)\n",
    "ax1.set_title(\"全球确诊病例累计\", size=13)\n",
    "ax1.set_ylabel(\"病例数\", size=13)\n",
    "ax1.set_xlabel(\"日期\", size=13)\n",
    "fatalities_total_date.plot(ax=ax2, color='orange')\n",
    "ax2.set_title(\"全球死亡病例累计\", size=13)\n",
    "ax2.set_ylabel(\"病例数\", size=13)\n",
    "ax2.set_xlabel(\"日期\", size=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '日期')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confirmed_country_noChina = train[train['国家/地区']!='China'].groupby(['国家/地区', 'Province_State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country_noChina = train[train['国家/地区']!='China'].groupby(['国家/地区', 'Province_State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date_noChina = train[train['国家/地区']!='China'].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date_noChina = train[train['国家/地区']!='China'].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_noChina = confirmed_total_date_noChina.join(fatalities_total_date_noChina)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17,7))\n",
    "total_date_noChina.plot(ax=ax1)\n",
    "ax1.set_title(\"中国之外的全球确诊病例数\", size=13)\n",
    "ax1.set_ylabel(\"病例数\", size=13)\n",
    "ax1.set_xlabel(\"日期\", size=13)\n",
    "fatalities_total_date_noChina.plot(ax=ax2, color='orange')\n",
    "ax2.set_title(\"中国之外的全球死亡病例数\", size=13)\n",
    "ax2.set_ylabel(\"病例数\", size=13)\n",
    "ax2.set_xlabel(\"日期\", size=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '日期')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confirmed_country_China = train[train['国家/地区']=='China'].groupby(['国家/地区', 'Province_State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country_China = train[train['国家/地区']=='China'].groupby(['国家/地区', 'Province_State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date_China = train[train['国家/地区']=='China'].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date_China = train[train['国家/地区']=='China'].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_China = confirmed_total_date_China.join(fatalities_total_date_China)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17,7))\n",
    "total_date_China.plot(ax=ax1)\n",
    "ax1.set_title(\"中国确诊病例数\", size=13)\n",
    "ax1.set_ylabel(\"病例数\", size=13)\n",
    "ax1.set_xlabel(\"日期\", size=13)\n",
    "fatalities_total_date_China.plot(ax=ax2, color='orange')\n",
    "ax2.set_title(\"中国死亡病例数\", size=13)\n",
    "ax2.set_ylabel(\"病例数\", size=13)\n",
    "ax2.set_xlabel(\"日期\", size=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'新加坡'}, xlabel='日期'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confirmed_country_Italy = train[train['国家/地区']=='Italy'].groupby(['国家/地区', 'Province_State']).agg({'确诊病例数':['sum']})\n",
    "#死亡人数_country_Italy = train[train['国家/地区']=='Italy'].groupby(['国家/地区', 'Province_State']).agg({'死亡人数':['sum']})\n",
    "confirmed_total_date_Italy = train[train['国家/地区']=='Italy'].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "Fatalities_total_date_Italy = train[train['国家/地区']=='Italy'].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Italy = confirmed_total_date_Italy.join(Fatalities_total_date_Italy)\n",
    "\n",
    "#confirmed_country_Spain = train[train['国家/地区']=='Spain'].groupby(['国家/地区', 'Province_State']).agg({'确诊病例数':['sum']})\n",
    "#死亡人数_country_Spain = train[train['国家/地区']=='Spain'].groupby(['国家/地区', 'Province_State']).agg({'死亡人数':['sum']})\n",
    "confirmed_total_date_Spain = train[train['国家/地区']=='Spain'].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "Fatalities_total_date_Spain = train[train['国家/地区']=='Spain'].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Spain = confirmed_total_date_Spain.join(Fatalities_total_date_Spain)\n",
    "\n",
    "#confirmed_country_UK = train[train['国家/地区']=='United Kingdom'].groupby(['国家/地区', 'Province_State']).agg({'确诊病例数':['sum']})\n",
    "#死亡人数_country_UK = train[train['国家/地区']=='United Kingdom'].groupby(['国家/地区', 'Province_State']).agg({'死亡人数':['sum']})\n",
    "confirmed_total_date_UK = train[train['国家/地区']=='United Kingdom'].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "Fatalities_total_date_UK = train[train['国家/地区']=='United Kingdom'].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_UK = confirmed_total_date_UK.join(Fatalities_total_date_UK)\n",
    "\n",
    "#confirmed_country_Australia = train[train['国家/地区']=='Australia'].groupby(['国家/地区', 'Province_State']).agg({'确诊病例数':['sum']})\n",
    "#死亡人数_country_Australia = train[train['国家/地区']=='Australia'].groupby(['国家/地区', 'Province_State']).agg({'死亡人数':['sum']})\n",
    "confirmed_total_date_Australia = train[train['国家/地区']=='Australia'].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "Fatalities_total_date_Australia = train[train['国家/地区']=='Australia'].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Australia = confirmed_total_date_Australia.join(Fatalities_total_date_Australia)\n",
    "\n",
    "#confirmed_country_Singapore = train[train['国家/地区']=='Singapore'].groupby(['国家/地区', 'Province_State']).agg({'确诊病例数':['sum']})\n",
    "#死亡人数_country_Singapore = train[train['国家/地区']=='Singapore'].groupby(['国家/地区', 'Province_State']).agg({'死亡人数':['sum']})\n",
    "confirmed_total_date_Singapore = train[train['国家/地区']=='Singapore'].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "Fatalities_total_date_Singapore = train[train['国家/地区']=='Singapore'].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Singapore = confirmed_total_date_Singapore.join(Fatalities_total_date_Singapore)\n",
    "\n",
    "plt.figure(figsize=(17,10))\n",
    "plt.subplot(2, 2, 1)\n",
    "total_date_Italy.plot(ax=plt.gca(), title='意大利')\n",
    "plt.ylabel(\"确诊感染数\", size=13)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "total_date_Spain.plot(ax=plt.gca(), title='西班牙')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "total_date_UK.plot(ax=plt.gca(), title='英国')\n",
    "plt.ylabel(\"确诊感染数\", size=13)\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "total_date_Singapore.plot(ax=plt.gca(), title='新加坡')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.05)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_italy = 60486683.\n",
    "pop_spain = 46749696.\n",
    "pop_UK = 67784927.\n",
    "pop_singapore = 5837230.\n",
    "\n",
    "total_date_Italy['确诊病例数'] = total_date_Italy['确诊病例数']/pop_italy*100.\n",
    "total_date_Italy['死亡人数'] = total_date_Italy['死亡人数']/pop_italy*100.\n",
    "total_date_Spain['确诊病例数'] = total_date_Spain['确诊病例数']/pop_spain*100.\n",
    "total_date_Spain['死亡人数'] = total_date_Spain['死亡人数']/pop_spain*100.\n",
    "total_date_UK['确诊病例数'] = total_date_UK['确诊病例数']/pop_UK*100.\n",
    "total_date_UK['死亡人数'] = total_date_UK['死亡人数']/pop_UK*100.\n",
    "total_date_Singapore['确诊病例数'] = total_date_Singapore['确诊病例数']/pop_singapore*100.\n",
    "total_date_Singapore['死亡人数'] = total_date_Singapore['死亡人数']/pop_singapore*100.\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(2, 2, 1)\n",
    "total_date_Italy['确诊病例数'].plot(ax=plt.gca(), title='意大利')\n",
    "plt.ylabel(\"人口感染比例\")\n",
    "plt.ylim(0, 0.5)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "total_date_Spain['确诊病例数'].plot(ax=plt.gca(), title='西班牙')\n",
    "plt.ylim(0, 0.5)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "total_date_UK['确诊病例数'].plot(ax=plt.gca(), title='英国')\n",
    "plt.ylabel(\"人口感染比例\")\n",
    "plt.ylim(0, 0.1)\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "total_date_Singapore['确诊病例数'].plot(ax=plt.gca(), title='新加坡')\n",
    "plt.ylim(0, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confirmed_country_Italy = train[(train['国家/地区']=='Italy') & train['ConfirmedCases']!=0].groupby(['国家/地区', 'Province_State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country_Italy = train[(train['国家/地区']=='Italy') & train['ConfirmedCases']!=0].groupby(['国家/地区', 'Province_State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date_Italy = train[(train['国家/地区']=='Italy') & train['确诊病例数']!=0].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date_Italy = train[(train['国家/地区']=='Italy') & train['确诊病例数']!=0].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Italy = confirmed_total_date_Italy.join(fatalities_total_date_Italy)\n",
    "\n",
    "#confirmed_country_Spain = train[(train['国家/地区']=='Spain') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country_Spain = train[(train['国家/地区']=='Spain') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date_Spain = train[(train['国家/地区']=='Spain') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date_Spain = train[(train['国家/地区']=='Spain') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Spain = confirmed_total_date_Spain.join(fatalities_total_date_Spain)\n",
    "\n",
    "#confirmed_country_UK = train[(train['国家/地区']=='United Kingdom') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country_UK = train[(train['国家/地区']=='United Kingdom') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date_UK = train[(train['国家/地区']=='United Kingdom') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date_UK = train[(train['国家/地区']=='United Kingdom') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_UK = confirmed_total_date_UK.join(fatalities_total_date_UK)\n",
    "\n",
    "#confirmed_country_Australia = train[(train['国家/地区']=='Australia') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country_Australia = train[(train['国家/地区']=='Australia') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date_Australia = train[(train['国家/地区']=='Australia') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date_Australia = train[(train['国家/地区']=='Australia') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Australia = confirmed_total_date_Australia.join(fatalities_total_date_Australia)\n",
    "\n",
    "#confirmed_country_Singapore = train[(train['国家/地区']=='Singapore') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'ConfirmedCases':['sum']})\n",
    "#fatalities_country_Singapore = train[(train['国家/地区']=='Singapore') & (train['ConfirmedCases']!=0)].groupby(['国家/地区', 'Province_State']).agg({'Fatalities':['sum']})\n",
    "confirmed_total_date_Singapore = train[(train['国家/地区']=='Singapore') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'确诊病例数':['sum']})\n",
    "fatalities_total_date_Singapore = train[(train['国家/地区']=='Singapore') & (train['确诊病例数']!=0)].groupby(['日期']).agg({'死亡人数':['sum']})\n",
    "total_date_Singapore = confirmed_total_date_Singapore.join(fatalities_total_date_Singapore)\n",
    "\n",
    "italy = [i for i in total_date_Italy['确诊病例数']['sum'].values]\n",
    "italy_30 = italy[0:70] \n",
    "spain = [i for i in total_date_Spain['确诊病例数']['sum'].values]\n",
    "spain_30 = spain[0:70] \n",
    "UK = [i for i in total_date_UK['确诊病例数']['sum'].values]\n",
    "UK_30 = UK[0:70] \n",
    "singapore = [i for i in total_date_Singapore['确诊病例数']['sum'].values]\n",
    "singapore_30 = singapore[0:70] \n",
    "\n",
    "\n",
    "# Plots\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(italy_30)\n",
    "plt.plot(spain_30)\n",
    "plt.plot(UK_30)\n",
    "plt.plot(singapore_30)\n",
    "plt.legend([\"意大利\", \"西班牙\", \"英国\", \"新加坡\"], loc='upper left')\n",
    "plt.title(\"首例感染以来的新冠感染数量\", size=15)\n",
    "plt.xlabel(\"天数\", size=13)\n",
    "plt.ylabel(\"感染数\", size=13)\n",
    "plt.ylim(0, 130000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# 易感方程\n",
    "def fa(N, a, b, beta):\n",
    "    fa = -beta*a*b\n",
    "    return fa\n",
    "\n",
    "# 感染方程\n",
    "def fb(N, a, b, beta, gamma):\n",
    "    fb = beta*a*b - gamma*b\n",
    "    return fb\n",
    "\n",
    "# 治愈/死亡 方程\n",
    "def fc(N, b, gamma):\n",
    "    fc = gamma*b\n",
    "    return fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3维的4r阶Runge-Kutta方法（易感性a，受感染的b和恢复的r）\n",
    "def rK4(N, a, b, c, fa, fb, fc, beta, gamma, hs):\n",
    "    a1 = fa(N, a, b, beta)*hs\n",
    "    b1 = fb(N, a, b, beta, gamma)*hs\n",
    "    c1 = fc(N, b, gamma)*hs\n",
    "    ak = a + a1*0.5\n",
    "    bk = b + b1*0.5\n",
    "    ck = c + c1*0.5\n",
    "    a2 = fa(N, ak, bk, beta)*hs\n",
    "    b2 = fb(N, ak, bk, beta, gamma)*hs\n",
    "    c2 = fc(N, bk, gamma)*hs\n",
    "    ak = a + a2*0.5\n",
    "    bk = b + b2*0.5\n",
    "    ck = c + c2*0.5\n",
    "    a3 = fa(N, ak, bk, beta)*hs\n",
    "    b3 = fb(N, ak, bk, beta, gamma)*hs\n",
    "    c3 = fc(N, bk, gamma)*hs\n",
    "    ak = a + a3\n",
    "    bk = b + b3\n",
    "    ck = c + c3\n",
    "    a4 = fa(N, ak, bk, beta)*hs\n",
    "    b4 = fb(N, ak, bk, beta, gamma)*hs\n",
    "    c4 = fc(N, bk, gamma)*hs\n",
    "    a = a + (a1 + 2*(a2 + a3) + a4)/6\n",
    "    b = b + (b1 + 2*(b2 + b3) + b4)/6\n",
    "    c = c + (c1 + 2*(c2 + c3) + c4)/6\n",
    "    return a, b, c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SIR(N, b0, beta, gamma, hs):\n",
    "    \n",
    "    \"\"\"\n",
    "    N = 人口总数\n",
    "    beta = 易感性 (S)->感染 (I) 的转化率\n",
    "    gamma = 感染 (I)->治愈/死亡 (R) 的转化率\n",
    "    k =  表示网络的恒定度分布（其中     找到具有不同连通性的节点的概率呈指数级衰减\n",
    "    hs = 数值积分的跳跃步骤\n",
    "    \"\"\"\n",
    "    \n",
    "    # 初始状况\n",
    "    a = float(N-1)/N -b0\n",
    "    b = float(1)/N +b0\n",
    "    c = 0.\n",
    "\n",
    "    sus, inf, rec= [],[],[]\n",
    "    for i in range(10000): # 运行一定数量的时间\n",
    "        sus.append(a)\n",
    "        inf.append(b)\n",
    "        rec.append(c)\n",
    "        a,b,c = rK4(N, a, b, c, fa, fb, fc, beta, gamma, hs)\n",
    "\n",
    "    return sus, inf, rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 模型参数\n",
    "N = 7800*(10**6)\n",
    "b0 = 0\n",
    "beta = 0.7\n",
    "gamma = 0.2\n",
    "hs = 0.1\n",
    "\n",
    "sus, inf, rec = SIR(N, b0, beta, gamma, hs)\n",
    "\n",
    "f = plt.figure(figsize=(8,5)) \n",
    "plt.plot(sus, 'b.', label='易感者');\n",
    "plt.plot(inf, 'r.', label='感染者');\n",
    "plt.plot(rec, 'c.', label='治愈者/死亡者');\n",
    "plt.title(\"SIR 模型\")\n",
    "plt.xlabel(\"时间\", fontsize=10);\n",
    "plt.ylabel(\"人口比例\", fontsize=10);\n",
    "plt.legend(loc='best')\n",
    "plt.xlim(0,1000)\n",
    "plt.savefig('SIR_example.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\scipy\\integrate\\odepack.py:247: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
      "  warnings.warn(warning_msg, ODEintWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x1584 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数: beta = 9.16432794095478  and gamma =  8.99127275140516\n"
     ]
    }
   ],
   "source": [
    "population = float(46750238)\n",
    "country_df = pd.DataFrame()\n",
    "country_df['确诊病例数'] = train.loc[train['国家/地区']=='Spain']['确诊病例数'].diff().fillna(0)\n",
    "country_df = country_df[10:]\n",
    "country_df['day_count'] = list(range(1,len(country_df)+1))\n",
    "\n",
    "ydata = [i for i in country_df['确诊病例数']]\n",
    "xdata = country_df.day_count\n",
    "ydata = np.array(ydata, dtype=float)\n",
    "xdata = np.array(xdata, dtype=float)\n",
    "\n",
    "N = population\n",
    "inf0 = ydata[0]\n",
    "sus0 = N - inf0\n",
    "rec0 = 0.0\n",
    "\n",
    "def sir_model(y, x, beta, gamma):\n",
    "    sus = -beta * y[0] * y[1] / N\n",
    "    rec = gamma * y[1]\n",
    "    inf = -(sus + rec)\n",
    "    return sus, inf, rec\n",
    "\n",
    "def fit_odeint(x, beta, gamma):\n",
    "    return integrate.odeint(sir_model, (sus0, inf0, rec0), x, args=(beta, gamma))[:,1]\n",
    "\n",
    "popt, pcov = optimize.curve_fit(fit_odeint, xdata, ydata, maxfev=500000)\n",
    "fitted = fit_odeint(xdata, *popt)\n",
    "\n",
    "plt.plot(xdata, ydata, 'o')\n",
    "plt.plot(xdata, fitted)\n",
    "plt.title(\"SIR 模型根据西班牙感染者数量进行拟合\")\n",
    "plt.ylabel(\"感染人口数\")\n",
    "plt.xlabel(\"天数\")\n",
    "plt.show()\n",
    "print(\"最佳参数: beta =\", popt[0], \" and gamma = \", popt[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>省份/州</th>\n",
       "      <th>国家/地区</th>\n",
       "      <th>日期</th>\n",
       "      <th>确诊病例数</th>\n",
       "      <th>死亡人数</th>\n",
       "      <th>预测ID</th>\n",
       "      <th>天数</th>\n",
       "      <th>日</th>\n",
       "      <th>月</th>\n",
       "      <th>年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13454</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13455.0</td>\n",
       "      <td>108</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13455</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13456.0</td>\n",
       "      <td>109</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13456</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13457.0</td>\n",
       "      <td>110</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13457</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13458.0</td>\n",
       "      <td>111</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13458</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13459.0</td>\n",
       "      <td>112</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45072 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  省份/州        国家/地区         日期  确诊病例数  死亡人数     预测ID   天数   日  月  \\\n",
       "0      1.0  None  Afghanistan 2020-01-22    0.0   0.0     -1.0    0  22  1   \n",
       "1      2.0  None  Afghanistan 2020-01-23    0.0   0.0     -1.0    1  23  1   \n",
       "2      3.0  None  Afghanistan 2020-01-24    0.0   0.0     -1.0    2  24  1   \n",
       "3      4.0  None  Afghanistan 2020-01-25    0.0   0.0     -1.0    3  25  1   \n",
       "4      5.0  None  Afghanistan 2020-01-26    0.0   0.0     -1.0    4  26  1   \n",
       "...    ...   ...          ...        ...    ...   ...      ...  ...  .. ..   \n",
       "13454 -1.0  None     Zimbabwe 2020-05-10    0.0   0.0  13455.0  108  10  5   \n",
       "13455 -1.0  None     Zimbabwe 2020-05-11    0.0   0.0  13456.0  109  11  5   \n",
       "13456 -1.0  None     Zimbabwe 2020-05-12    0.0   0.0  13457.0  110  12  5   \n",
       "13457 -1.0  None     Zimbabwe 2020-05-13    0.0   0.0  13458.0  111  13  5   \n",
       "13458 -1.0  None     Zimbabwe 2020-05-14    0.0   0.0  13459.0  112  14  5   \n",
       "\n",
       "          年  \n",
       "0      2020  \n",
       "1      2020  \n",
       "2      2020  \n",
       "3      2020  \n",
       "4      2020  \n",
       "...     ...  \n",
       "13454  2020  \n",
       "13455  2020  \n",
       "13456  2020  \n",
       "13457  2020  \n",
       "13458  2020  \n",
       "\n",
       "[45072 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>省份/州</th>\n",
       "      <th>国家/地区</th>\n",
       "      <th>日期</th>\n",
       "      <th>确诊病例数</th>\n",
       "      <th>死亡人数</th>\n",
       "      <th>预测ID</th>\n",
       "      <th>天数</th>\n",
       "      <th>日</th>\n",
       "      <th>月</th>\n",
       "      <th>年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, 省份/州, 国家/地区, 日期, 确诊病例数, 死亡人数, 预测ID, 天数, 日, 月, 年]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 合并训练和测试，排除重叠\n",
    "dates_overlap = ['2020-04-01', '2020-04-02', '2020-04-03', '2020-04-04', '2020-04-05', '2020-04-06', '2020-04-07', '2020-04-08',\n",
    "                 '2020-04-09', '2020-04-10', '2020-04-11', '2020-04-12', '2020-04-13', '2020-04-14']\n",
    "train2 = train.loc[~train['日期'].isin(dates_overlap)]\n",
    "all_data = pd.concat([train2, test], axis = 0, sort=False)\n",
    "\n",
    "# 仔细检查2020-03-11之后没有已知的确诊病例和死亡人数\n",
    "all_data.loc[all_data['日期'] >= '2020-04-01', '确诊病例数'] = 0\n",
    "all_data.loc[all_data['日期'] >= '2020-04-01', '死亡人数'] = 0\n",
    "all_data['日期'] = pd.to_datetime(all_data['日期'])\n",
    "\n",
    "#LabelEncoder 将日期编号为连续自然数 表示“天数”\n",
    "le = preprocessing.LabelEncoder()\n",
    "# 创建日期列\n",
    "all_data['天数'] = le.fit_transform(all_data['日期'])\n",
    "all_data['日'] = all_data['日期'].dt.day\n",
    "all_data['月'] = all_data['日期'].dt.month\n",
    "all_data['年'] = all_data['日期'].dt.year\n",
    "\n",
    "# 给定我们合并训练测试数据集的空值\n",
    "all_data['省份/州'].fillna(\"None\", inplace=True)\n",
    "all_data['确诊病例数'].fillna(0, inplace=True)\n",
    "all_data['死亡人数'].fillna(0, inplace=True)\n",
    "all_data['ID'].fillna(-1, inplace=True)\n",
    "all_data['预测ID'].fillna(-1, inplace=True)\n",
    "\n",
    "display(all_data)\n",
    "display(all_data.loc[all_data['日期'] == '2020-04-01'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0\n",
      "ID     0\n",
      "省份/州   0\n",
      "国家/地区  0\n",
      "日期     0\n",
      "确诊病例数  0\n",
      "死亡人数   0\n",
      "预测ID   0\n",
      "天数     0\n",
      "日      0\n",
      "月      0\n",
      "年      0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "missings_count = {col:all_data[col].isnull().sum() for col in all_data.columns}\n",
    "missings = pd.DataFrame.from_dict(missings_count, orient='index')\n",
    "print(missings.nlargest(30, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "花费的时间为:  2.0246236324310303\n"
     ]
    }
   ],
   "source": [
    "def calculate_lag(df, lag_list, column):\n",
    "    for lag in lag_list:\n",
    "        column_lag = column + \"_\" + str(lag)\n",
    "        df[column_lag] = df.groupby(['国家/地区', '省份/州'])[column].shift(lag, fill_value=0)\n",
    "    return df\n",
    "\n",
    "def calculate_trend(df, lag_list, column):\n",
    "    for lag in lag_list:\n",
    "        trend_column_lag = \"Trend_\" + column + \"_\" + str(lag)\n",
    "        df[trend_column_lag] = (df.groupby(['国家/地区', '省份/州'])[column].shift(0, fill_value=0) - \n",
    "                                df.groupby(['国家/地区', '省份/州'])[column].shift(lag, fill_value=0))/df.groupby(['国家/地区', '省份/州'])[column].shift(lag, fill_value=0.001)\n",
    "    return df\n",
    "\n",
    "\n",
    "ts = time.time()\n",
    "all_data = calculate_lag(all_data.reset_index(), range(1,7), '确诊病例数')\n",
    "all_data = calculate_lag(all_data, range(1,7), '死亡人数')\n",
    "all_data = calculate_trend(all_data, range(1,7), '确诊病例数')\n",
    "all_data = calculate_trend(all_data, range(1,7), '死亡人数')\n",
    "all_data.replace([np.inf, -np.inf], 0, inplace=True)\n",
    "all_data.fillna(0, inplace=True)\n",
    "print(\"花费的时间为: \", time.time()-ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>省份/州</th>\n",
       "      <th>国家/地区</th>\n",
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       "      <th>天数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22462</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22463</th>\n",
       "      <td>25572.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>165.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22464</th>\n",
       "      <td>25573.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>222.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22465</th>\n",
       "      <td>25574.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22466</th>\n",
       "      <td>25575.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22467</th>\n",
       "      <td>25576.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22468</th>\n",
       "      <td>25577.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>673.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22469</th>\n",
       "      <td>25578.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22470</th>\n",
       "      <td>25579.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22471</th>\n",
       "      <td>25580.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  省份/州  国家/地区         日期   确诊病例数  死亡人数  预测ID  天数\n",
       "22462  25571.0  None  Spain 2020-03-02   120.0   0.0  -1.0  40\n",
       "22463  25572.0  None  Spain 2020-03-03   165.0   1.0  -1.0  41\n",
       "22464  25573.0  None  Spain 2020-03-04   222.0   2.0  -1.0  42\n",
       "22465  25574.0  None  Spain 2020-03-05   259.0   3.0  -1.0  43\n",
       "22466  25575.0  None  Spain 2020-03-06   400.0   5.0  -1.0  44\n",
       "22467  25576.0  None  Spain 2020-03-07   500.0  10.0  -1.0  45\n",
       "22468  25577.0  None  Spain 2020-03-08   673.0  17.0  -1.0  46\n",
       "22469  25578.0  None  Spain 2020-03-09  1073.0  28.0  -1.0  47\n",
       "22470  25579.0  None  Spain 2020-03-10  1695.0  35.0  -1.0  48\n",
       "22471  25580.0  None  Spain 2020-03-11  2277.0  54.0  -1.0  49"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[all_data['国家/地区']=='Spain'].iloc[40:50][['ID', '省份/州', '国家/地区', '日期',\n",
    "       '确诊病例数', '死亡人数', '预测ID', '天数']]\n",
    "\n",
    "# , 'ConfirmedCases_1',\n",
    "#        'ConfirmedCases_2', 'ConfirmedCases_3', 'Fatalities_1', 'Fatalities_2',\n",
    "#        'Fatalities_3']]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "清理国家细节数据集\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家 (或地区)</th>\n",
       "      <th>人口 (2020)</th>\n",
       "      <th>人口密度</th>\n",
       "      <th>国土面积</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>1440297825</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>38</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>1382345085</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>331341050</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>274021604</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>221612785</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Montserrat</td>\n",
       "      <td>4993</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Falkland Islands</td>\n",
       "      <td>3497</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>19</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Niue</td>\n",
       "      <td>1628</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>19</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>Tokelau</td>\n",
       "      <td>1360</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Holy See</td>\n",
       "      <td>801</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             国家 (或地区)   人口 (2020)  人口密度     国土面积  中位年龄  城市人口比例\n",
       "0               China  1440297825   153  9388211    38      61\n",
       "1               India  1382345085   464  2973190    28      35\n",
       "2                  US   331341050    36  9147420    38      83\n",
       "3           Indonesia   274021604   151  1811570    30      56\n",
       "4            Pakistan   221612785   287   770880    23      35\n",
       "..                ...         ...   ...      ...   ...     ...\n",
       "230        Montserrat        4993    50      100    19      10\n",
       "231  Falkland Islands        3497     0    12170    19      66\n",
       "232              Niue        1628     6      260    19      46\n",
       "233           Tokelau        1360   136       10    19       0\n",
       "234          Holy See         801  2003        0    19      57\n",
       "\n",
       "[235 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joined dataset\n"
     ]
    },
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>39074280.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>652860.0</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2020</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>39074280.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>652860.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>45067</th>\n",
       "      <td>13454</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13455.0</td>\n",
       "      <td>108</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zimbabwe</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>45068</th>\n",
       "      <td>13455</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>2020-05-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13456.0</td>\n",
       "      <td>109</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
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       "    <tr>\n",
       "      <th>45069</th>\n",
       "      <td>13456</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-12</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>13457.0</td>\n",
       "      <td>110</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45070</th>\n",
       "      <td>13457</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13458.0</td>\n",
       "      <td>111</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45071</th>\n",
       "      <td>13458</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-05-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13459.0</td>\n",
       "      <td>112</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45072 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index   ID  省份/州        国家/地区         日期  确诊病例数  死亡人数     预测ID   天数  \\\n",
       "0          0  1.0  None  Afghanistan 2020-01-22    0.0   0.0     -1.0    0   \n",
       "1          1  2.0  None  Afghanistan 2020-01-23    0.0   0.0     -1.0    1   \n",
       "2          2  3.0  None  Afghanistan 2020-01-24    0.0   0.0     -1.0    2   \n",
       "3          3  4.0  None  Afghanistan 2020-01-25    0.0   0.0     -1.0    3   \n",
       "4          4  5.0  None  Afghanistan 2020-01-26    0.0   0.0     -1.0    4   \n",
       "...      ...  ...   ...          ...        ...    ...   ...      ...  ...   \n",
       "45067  13454 -1.0  None     Zimbabwe 2020-05-10    0.0   0.0  13455.0  108   \n",
       "45068  13455 -1.0  None     Zimbabwe 2020-05-11    0.0   0.0  13456.0  109   \n",
       "45069  13456 -1.0  None     Zimbabwe 2020-05-12    0.0   0.0  13457.0  110   \n",
       "45070  13457 -1.0  None     Zimbabwe 2020-05-13    0.0   0.0  13458.0  111   \n",
       "45071  13458 -1.0  None     Zimbabwe 2020-05-14    0.0   0.0  13459.0  112   \n",
       "\n",
       "        日  月     年  确诊病例数_1  确诊病例数_2  确诊病例数_3  确诊病例数_4  确诊病例数_5  确诊病例数_6  ...  \\\n",
       "0      22  1  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "1      23  1  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "2      24  1  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "3      25  1  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "4      26  1  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "...    .. ..   ...      ...      ...      ...      ...      ...      ...  ...   \n",
       "45067  10  5  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45068  11  5  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45069  12  5  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45070  13  5  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45071  14  5  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "\n",
       "       Trend_确诊病例数_1  Trend_确诊病例数_2  Trend_确诊病例数_3  Trend_确诊病例数_4  \\\n",
       "0                0.0            0.0            0.0            0.0   \n",
       "1                0.0            0.0            0.0            0.0   \n",
       "2                0.0            0.0            0.0            0.0   \n",
       "3                0.0            0.0            0.0            0.0   \n",
       "4                0.0            0.0            0.0            0.0   \n",
       "...              ...            ...            ...            ...   \n",
       "45067            0.0            0.0            0.0            0.0   \n",
       "45068            0.0            0.0            0.0            0.0   \n",
       "45069            0.0            0.0            0.0            0.0   \n",
       "45070            0.0            0.0            0.0            0.0   \n",
       "45071            0.0            0.0            0.0            0.0   \n",
       "\n",
       "       Trend_确诊病例数_5  Trend_确诊病例数_6  Trend_死亡人数_1  Trend_死亡人数_2  Trend_死亡人数_3  \\\n",
       "0                0.0            0.0           0.0           0.0           0.0   \n",
       "1                0.0            0.0           0.0           0.0           0.0   \n",
       "2                0.0            0.0           0.0           0.0           0.0   \n",
       "3                0.0            0.0           0.0           0.0           0.0   \n",
       "4                0.0            0.0           0.0           0.0           0.0   \n",
       "...              ...            ...           ...           ...           ...   \n",
       "45067            0.0            0.0           0.0           0.0           0.0   \n",
       "45068            0.0            0.0           0.0           0.0           0.0   \n",
       "45069            0.0            0.0           0.0           0.0           0.0   \n",
       "45070            0.0            0.0           0.0           0.0           0.0   \n",
       "45071            0.0            0.0           0.0           0.0           0.0   \n",
       "\n",
       "       Trend_死亡人数_4  Trend_死亡人数_5  Trend_死亡人数_6     国家 (或地区)   人口 (2020)  \\\n",
       "0               0.0           0.0           0.0  Afghanistan  39074280.0   \n",
       "1               0.0           0.0           0.0  Afghanistan  39074280.0   \n",
       "2               0.0           0.0           0.0  Afghanistan  39074280.0   \n",
       "3               0.0           0.0           0.0  Afghanistan  39074280.0   \n",
       "4               0.0           0.0           0.0  Afghanistan  39074280.0   \n",
       "...             ...           ...           ...          ...         ...   \n",
       "45067           0.0           0.0           0.0     Zimbabwe  14899771.0   \n",
       "45068           0.0           0.0           0.0     Zimbabwe  14899771.0   \n",
       "45069           0.0           0.0           0.0     Zimbabwe  14899771.0   \n",
       "45070           0.0           0.0           0.0     Zimbabwe  14899771.0   \n",
       "45071           0.0           0.0           0.0     Zimbabwe  14899771.0   \n",
       "\n",
       "       人口密度      国土面积  中位年龄  城市人口比例  \n",
       "0      60.0  652860.0  18.0    25.0  \n",
       "1      60.0  652860.0  18.0    25.0  \n",
       "2      60.0  652860.0  18.0    25.0  \n",
       "3      60.0  652860.0  18.0    25.0  \n",
       "4      60.0  652860.0  18.0    25.0  \n",
       "...     ...       ...   ...     ...  \n",
       "45067  38.0  386850.0  19.0    38.0  \n",
       "45068  38.0  386850.0  19.0    38.0  \n",
       "45069  38.0  386850.0  19.0    38.0  \n",
       "45070  38.0  386850.0  19.0    38.0  \n",
       "45071  38.0  386850.0  19.0    38.0  \n",
       "\n",
       "[45072 rows x 42 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已编码的数据集\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
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       "      <th>人口密度</th>\n",
       "      <th>国土面积</th>\n",
       "      <th>中位年龄</th>\n",
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       "      <td>39074280.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>652860.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83</td>\n",
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       "      <td>2020-01-23</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39074280.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>652860.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39074280.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>652860.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45067</th>\n",
       "      <td>13454</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>83</td>\n",
       "      <td>183</td>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13455.0</td>\n",
       "      <td>108</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45068</th>\n",
       "      <td>13455</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>83</td>\n",
       "      <td>183</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13456.0</td>\n",
       "      <td>109</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45069</th>\n",
       "      <td>13456</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>83</td>\n",
       "      <td>183</td>\n",
       "      <td>2020-05-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13457.0</td>\n",
       "      <td>110</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45070</th>\n",
       "      <td>13457</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>83</td>\n",
       "      <td>183</td>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13458.0</td>\n",
       "      <td>111</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45071</th>\n",
       "      <td>13458</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>83</td>\n",
       "      <td>183</td>\n",
       "      <td>2020-05-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13459.0</td>\n",
       "      <td>112</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14899771.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>386850.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45072 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index   ID  省份/州  国家/地区         日期  确诊病例数  死亡人数     预测ID   天数   日  月  \\\n",
       "0          0  1.0    83      0 2020-01-22    0.0   0.0     -1.0    0  22  1   \n",
       "1          1  2.0    83      0 2020-01-23    0.0   0.0     -1.0    1  23  1   \n",
       "2          2  3.0    83      0 2020-01-24    0.0   0.0     -1.0    2  24  1   \n",
       "3          3  4.0    83      0 2020-01-25    0.0   0.0     -1.0    3  25  1   \n",
       "4          4  5.0    83      0 2020-01-26    0.0   0.0     -1.0    4  26  1   \n",
       "...      ...  ...   ...    ...        ...    ...   ...      ...  ...  .. ..   \n",
       "45067  13454 -1.0    83    183 2020-05-10    0.0   0.0  13455.0  108  10  5   \n",
       "45068  13455 -1.0    83    183 2020-05-11    0.0   0.0  13456.0  109  11  5   \n",
       "45069  13456 -1.0    83    183 2020-05-12    0.0   0.0  13457.0  110  12  5   \n",
       "45070  13457 -1.0    83    183 2020-05-13    0.0   0.0  13458.0  111  13  5   \n",
       "45071  13458 -1.0    83    183 2020-05-14    0.0   0.0  13459.0  112  14  5   \n",
       "\n",
       "          年  确诊病例数_1  确诊病例数_2  确诊病例数_3  确诊病例数_4  确诊病例数_5  确诊病例数_6  ...  \\\n",
       "0      2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "1      2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "2      2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "3      2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "4      2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "...     ...      ...      ...      ...      ...      ...      ...  ...   \n",
       "45067  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45068  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45069  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45070  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "45071  2020      0.0      0.0      0.0      0.0      0.0      0.0  ...   \n",
       "\n",
       "       死亡人数_6  Trend_确诊病例数_1  Trend_确诊病例数_2  Trend_确诊病例数_3  Trend_确诊病例数_4  \\\n",
       "0         0.0            0.0            0.0            0.0            0.0   \n",
       "1         0.0            0.0            0.0            0.0            0.0   \n",
       "2         0.0            0.0            0.0            0.0            0.0   \n",
       "3         0.0            0.0            0.0            0.0            0.0   \n",
       "4         0.0            0.0            0.0            0.0            0.0   \n",
       "...       ...            ...            ...            ...            ...   \n",
       "45067     0.0            0.0            0.0            0.0            0.0   \n",
       "45068     0.0            0.0            0.0            0.0            0.0   \n",
       "45069     0.0            0.0            0.0            0.0            0.0   \n",
       "45070     0.0            0.0            0.0            0.0            0.0   \n",
       "45071     0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "       Trend_确诊病例数_5  Trend_确诊病例数_6  Trend_死亡人数_1  Trend_死亡人数_2  Trend_死亡人数_3  \\\n",
       "0                0.0            0.0           0.0           0.0           0.0   \n",
       "1                0.0            0.0           0.0           0.0           0.0   \n",
       "2                0.0            0.0           0.0           0.0           0.0   \n",
       "3                0.0            0.0           0.0           0.0           0.0   \n",
       "4                0.0            0.0           0.0           0.0           0.0   \n",
       "...              ...            ...           ...           ...           ...   \n",
       "45067            0.0            0.0           0.0           0.0           0.0   \n",
       "45068            0.0            0.0           0.0           0.0           0.0   \n",
       "45069            0.0            0.0           0.0           0.0           0.0   \n",
       "45070            0.0            0.0           0.0           0.0           0.0   \n",
       "45071            0.0            0.0           0.0           0.0           0.0   \n",
       "\n",
       "       Trend_死亡人数_4  Trend_死亡人数_5  Trend_死亡人数_6   人口 (2020)  人口密度      国土面积  \\\n",
       "0               0.0           0.0           0.0  39074280.0  60.0  652860.0   \n",
       "1               0.0           0.0           0.0  39074280.0  60.0  652860.0   \n",
       "2               0.0           0.0           0.0  39074280.0  60.0  652860.0   \n",
       "3               0.0           0.0           0.0  39074280.0  60.0  652860.0   \n",
       "4               0.0           0.0           0.0  39074280.0  60.0  652860.0   \n",
       "...             ...           ...           ...         ...   ...       ...   \n",
       "45067           0.0           0.0           0.0  14899771.0  38.0  386850.0   \n",
       "45068           0.0           0.0           0.0  14899771.0  38.0  386850.0   \n",
       "45069           0.0           0.0           0.0  14899771.0  38.0  386850.0   \n",
       "45070           0.0           0.0           0.0  14899771.0  38.0  386850.0   \n",
       "45071           0.0           0.0           0.0  14899771.0  38.0  386850.0   \n",
       "\n",
       "       中位年龄  城市人口比例  \n",
       "0      18.0    25.0  \n",
       "1      18.0    25.0  \n",
       "2      18.0    25.0  \n",
       "3      18.0    25.0  \n",
       "4      18.0    25.0  \n",
       "...     ...     ...  \n",
       "45067  19.0    38.0  \n",
       "45068  19.0    38.0  \n",
       "45069  19.0    38.0  \n",
       "45070  19.0    38.0  \n",
       "45071  19.0    38.0  \n",
       "\n",
       "[45072 rows x 41 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从数据文件中加载国家数据\n",
    "world_population = pd.read_csv(\"C:/Users/Auror/Desktop/covid19/population_by_country_2020.csv\")\n",
    "\n",
    "# 选择想要的字段并对其中的一些进行重命名\n",
    "world_population = world_population[['Country (or dependency)', 'Population (2020)', 'Density (P/Km²)', 'Land Area (Km²)', 'Med. Age', 'Urban Pop %']]\n",
    "world_population.columns = ['国家 (或地区)', '人口 (2020)', '人口密度', '国土面积', '中位年龄', '城市人口比例']\n",
    "\n",
    "# 替换国家名， United States 替换为 US\n",
    "world_population.loc[world_population['国家 (或地区)']=='United States', '国家 (或地区)'] = 'US'\n",
    "\n",
    "# 去掉‘城市人口比例’数据中的‘%’\n",
    "world_population['城市人口比例'] = world_population['城市人口比例'].str.rstrip('%')\n",
    "\n",
    "# 替换‘城市人口比例’和‘中位年龄’的空值为各自的众数，并取整\n",
    "world_population.loc[world_population['城市人口比例']=='N.A.', '城市人口比例'] = int(world_population.loc[world_population['城市人口比例']!='N.A.', '城市人口比例'].mode()[0])\n",
    "world_population['城市人口比例'] = world_population['城市人口比例'].astype('int16')\n",
    "world_population.loc[world_population['中位年龄']=='N.A.', '中位年龄'] = int(world_population.loc[world_population['中位年龄']!='N.A.', '中位年龄'].mode()[0])\n",
    "world_population['中位年龄'] = world_population['中位年龄'].astype('int16')\n",
    "\n",
    "print(\"清理国家细节数据集\")\n",
    "display(world_population)\n",
    "\n",
    "# 现在将数据集连接到我们之前的DataFrame并清除缺失（左连接中不匹配）-标签编码城市\n",
    "print(\"Joined dataset\")\n",
    "all_data = all_data.merge(world_population, left_on='国家/地区', right_on='国家 (或地区)', how='left')\n",
    "all_data[['人口 (2020)', '人口密度', '国土面积', '中位年龄', '城市人口比例']] = all_data[['人口 (2020)', '人口密度', '国土面积', '中位年龄', '城市人口比例']].fillna(0)\n",
    "display(all_data)\n",
    "\n",
    "print(\"已编码的数据集\")\n",
    "# 标签编码国家和省。用编号替换类别名\n",
    "all_data.drop('国家 (或地区)', inplace=True, axis=1)\n",
    "all_data['国家/地区'] = le.fit_transform(all_data['国家/地区'])\n",
    "# number_c是'国家/地区' 类别对应的编号\n",
    "number_c = all_data['国家/地区']\n",
    "# countries'国家/地区' 类别\n",
    "countries = le.inverse_transform(all_data['国家/地区'])\n",
    "# country_dict中的key是'国家/地区' 类别，value是对应的编号\n",
    "country_dict = dict(zip(countries, number_c)) \n",
    "all_data['省份/州'] = le.fit_transform(all_data['省份/州'])\n",
    "# number_p'省份/州' 类别对应的编号\n",
    "number_p = all_data['省份/州']\n",
    "# countries'省份/州' 类别\n",
    "province = le.inverse_transform(all_data['省份/州'])\n",
    "# province_dict中的key是'省份/州' 类别，value是对应的编号\n",
    "province_dict = dict(zip(province, number_p)) \n",
    "display(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '确诊病例数（对数处理）')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,6))\n",
    "\n",
    "# 天数 = 38 是 三月一日\n",
    "y1 = all_data[(all_data['国家/地区']==country_dict['Spain']) & (all_data['天数']>39) & (all_data['天数']<=49)][['确诊病例数']]\n",
    "x1 = range(0, len(y1))\n",
    "ax1.plot(x1, y1, 'bo--')\n",
    "ax1.set_title(\"西班牙确诊病例数量在疫情出现后39天-49天之间\")\n",
    "ax1.set_xlabel(\"天数\")\n",
    "ax1.set_ylabel(\"确诊病例数\")\n",
    "\n",
    "y2 = all_data[(all_data['国家/地区']==country_dict['Spain']) & (all_data['天数']>39) & (all_data['天数']<=49)][['确诊病例数']].apply(lambda x: np.log(x))\n",
    "x2 = range(0, len(y2))\n",
    "ax2.plot(x2, y2, 'bo--')\n",
    "ax2.set_title(\"西班牙确诊病例数量（对数处理）在疫情出现后39天-49天之间\")\n",
    "ax2.set_xlabel(\"天数\")\n",
    "ax2.set_ylabel(\"确诊病例数（对数处理）\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# 筛选所选功能\n",
    "data = all_data.copy()\n",
    "features = ['ID', '预测ID', '国家/地区', '省份/州', '确诊病例数', '死亡人数', \n",
    "       '天数']\n",
    "data = data[features]\n",
    "\n",
    "# 将对数转换应用于所有 确诊病例数 和 死亡人数 列（趋势除外）\n",
    "data[['确诊病例数', '死亡人数']] = data[['确诊病例数', '死亡人数']].astype('float64')\n",
    "data[['确诊病例数', '死亡人数']] = data[['确诊病例数', '死亡人数']].apply(lambda x: np.log1p(x))\n",
    "\n",
    "# 替换无穷大\n",
    "data.replace([np.inf, -np.inf], 0, inplace=True)\n",
    "\n",
    "\n",
    "# 将数据分割为训练集/测试集\n",
    "def split_data(df, train_lim, test_lim):\n",
    "    \n",
    "    df.loc[df['天数']<=train_lim , '预测ID'] = -1\n",
    "    df = df[df['天数']<=test_lim]\n",
    "    \n",
    "    # 训练集\n",
    "    x_train = df[df['预测ID'] == -1].drop(['确诊病例数', '死亡人数'], axis=1)\n",
    "    y_train_1 = df[df['预测ID'] == -1]['确诊病例数']\n",
    "    y_train_2 = df[df['预测ID'] == -1]['死亡人数']\n",
    "\n",
    "    # 测试集\n",
    "    x_test = df[df['预测ID'] != -1].drop(['确诊病例数', '死亡人数'], axis=1)\n",
    "\n",
    "    # 清理 ID 列 并将 预测ID 列作为索引\n",
    "    x_train.drop('ID', inplace=True, errors='ignore', axis=1)\n",
    "    x_train.drop('预测ID', inplace=True, errors='ignore', axis=1)\n",
    "    x_test.drop('ID', inplace=True, errors='ignore', axis=1)\n",
    "    x_test.drop('预测ID', inplace=True, errors='ignore', axis=1)\n",
    "    \n",
    "    return x_train, y_train_1, y_train_2, x_test\n",
    "\n",
    "\n",
    "# 线性回归模型\n",
    "def lin_reg(X_train, Y_train, X_test):\n",
    "    # 创建线性回归对象\n",
    "    regr = linear_model.LinearRegression()\n",
    "\n",
    "    # 用训练集来训练模型\n",
    "    regr.fit(X_train, Y_train)\n",
    "\n",
    "    # 用测试集来进行预测\n",
    "    y_pred = regr.predict(X_test)\n",
    "    \n",
    "    return regr, y_pred\n",
    "\n",
    "\n",
    "# 提交方程\n",
    "def get_submission(df, target1, target2):\n",
    "    \n",
    "    prediction_1 = df[target1]\n",
    "    prediction_2 = df[target2]\n",
    "\n",
    "    # 提交预测\n",
    "    prediction_1 = [int(item) for item in list(map(round, prediction_1))]\n",
    "    prediction_2 = [int(item) for item in list(map(round, prediction_2))]\n",
    "    \n",
    "    submission = pd.DataFrame({\n",
    "        \"预测ID\": df['预测ID'].astype('int32'), \n",
    "        \"确诊病例数\": prediction_1, \n",
    "        \"死亡人数\": prediction_2\n",
    "    })\n",
    "    submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 选择从3月1日到最后一天的训练（真实）数据更新\n",
    "dates_list = ['2020-03-01', '2020-03-02', '2020-03-03', '2020-03-04', '2020-03-05', '2020-03-06', '2020-03-07', '2020-03-08', '2020-03-09', \n",
    "                 '2020-03-10', '2020-03-11','2020-03-12','2020-03-13','2020-03-14','2020-03-15','2020-03-16','2020-03-17','2020-03-18',\n",
    "                 '2020-03-19','2020-03-20','2020-03-21','2020-03-22','2020-03-23', '2020-03-24', '2020-03-25', '2020-03-26', '2020-03-27', \n",
    "                 '2020-03-28', '2020-03-29', '2020-03-30', '2020-03-31', '2020-04-01', '2020-04-02', '2020-04-03', '2020-04-04', '2020-04-05', \n",
    "                 '2020-04-06', '2020-04-07', '2020-04-08', '2020-04-09', '2020-04-10', '2020-04-11', '2020-04-12', '2020-04-13', '2020-04-14']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ID</th>\n",
       "      <th>省份/州</th>\n",
       "      <th>国家/地区</th>\n",
       "      <th>日期</th>\n",
       "      <th>确诊病例数</th>\n",
       "      <th>死亡人数</th>\n",
       "      <th>预测ID</th>\n",
       "      <th>天数</th>\n",
       "      <th>日</th>\n",
       "      <th>月</th>\n",
       "      <th>年</th>\n",
       "      <th>确诊病例数_1</th>\n",
       "      <th>确诊病例数_2</th>\n",
       "      <th>确诊病例数_3</th>\n",
       "      <th>确诊病例数_4</th>\n",
       "      <th>确诊病例数_5</th>\n",
       "      <th>确诊病例数_6</th>\n",
       "      <th>...</th>\n",
       "      <th>死亡人数_6</th>\n",
       "      <th>Trend_确诊病例数_1</th>\n",
       "      <th>Trend_确诊病例数_2</th>\n",
       "      <th>Trend_确诊病例数_3</th>\n",
       "      <th>Trend_确诊病例数_4</th>\n",
       "      <th>Trend_确诊病例数_5</th>\n",
       "      <th>Trend_确诊病例数_6</th>\n",
       "      <th>Trend_死亡人数_1</th>\n",
       "      <th>Trend_死亡人数_2</th>\n",
       "      <th>Trend_死亡人数_3</th>\n",
       "      <th>Trend_死亡人数_4</th>\n",
       "      <th>Trend_死亡人数_5</th>\n",
       "      <th>Trend_死亡人数_6</th>\n",
       "      <th>人口 (2020)</th>\n",
       "      <th>人口密度</th>\n",
       "      <th>国土面积</th>\n",
       "      <th>中位年龄</th>\n",
       "      <th>城市人口比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22472</th>\n",
       "      <td>25580</td>\n",
       "      <td>25581.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.343363</td>\n",
       "      <td>1.122088</td>\n",
       "      <td>2.383358</td>\n",
       "      <td>3.554000</td>\n",
       "      <td>4.692500</td>\n",
       "      <td>0.018519</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>2.235294</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22473</th>\n",
       "      <td>25581</td>\n",
       "      <td>25582.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>51</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.297760</td>\n",
       "      <td>1.297760</td>\n",
       "      <td>2.086726</td>\n",
       "      <td>3.876048</td>\n",
       "      <td>6.774146</td>\n",
       "      <td>9.464000</td>\n",
       "      <td>1.418182</td>\n",
       "      <td>1.462963</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>6.823529</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22474</th>\n",
       "      <td>25582</td>\n",
       "      <td>25583.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.221521</td>\n",
       "      <td>1.806763</td>\n",
       "      <td>1.806763</td>\n",
       "      <td>2.770501</td>\n",
       "      <td>4.956198</td>\n",
       "      <td>8.496285</td>\n",
       "      <td>0.466165</td>\n",
       "      <td>2.545455</td>\n",
       "      <td>2.611111</td>\n",
       "      <td>4.571429</td>\n",
       "      <td>5.964286</td>\n",
       "      <td>10.470588</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22475</th>\n",
       "      <td>25583</td>\n",
       "      <td>25584.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>7798.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>53</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.220153</td>\n",
       "      <td>0.490443</td>\n",
       "      <td>2.424682</td>\n",
       "      <td>2.424682</td>\n",
       "      <td>3.600590</td>\n",
       "      <td>6.267474</td>\n",
       "      <td>0.482051</td>\n",
       "      <td>1.172932</td>\n",
       "      <td>4.254545</td>\n",
       "      <td>4.351852</td>\n",
       "      <td>7.257143</td>\n",
       "      <td>9.321429</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22476</th>\n",
       "      <td>25584</td>\n",
       "      <td>25585.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>54</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>7798.0</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.274942</td>\n",
       "      <td>0.555625</td>\n",
       "      <td>0.900229</td>\n",
       "      <td>3.366271</td>\n",
       "      <td>3.366271</td>\n",
       "      <td>4.865487</td>\n",
       "      <td>0.183391</td>\n",
       "      <td>0.753846</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>5.218182</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>8.771429</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22477</th>\n",
       "      <td>25585</td>\n",
       "      <td>25586.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>11748.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>7798.0</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.181654</td>\n",
       "      <td>0.506540</td>\n",
       "      <td>0.838210</td>\n",
       "      <td>1.245413</td>\n",
       "      <td>4.159420</td>\n",
       "      <td>4.159420</td>\n",
       "      <td>0.558480</td>\n",
       "      <td>0.844291</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>3.007519</td>\n",
       "      <td>8.690909</td>\n",
       "      <td>8.870370</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22478</th>\n",
       "      <td>25586</td>\n",
       "      <td>25587.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>11748.0</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>7798.0</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.184031</td>\n",
       "      <td>0.399115</td>\n",
       "      <td>0.783791</td>\n",
       "      <td>1.176498</td>\n",
       "      <td>1.658639</td>\n",
       "      <td>5.108915</td>\n",
       "      <td>0.168856</td>\n",
       "      <td>0.821637</td>\n",
       "      <td>1.155709</td>\n",
       "      <td>2.194872</td>\n",
       "      <td>3.684211</td>\n",
       "      <td>10.327273</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22479</th>\n",
       "      <td>25587</td>\n",
       "      <td>25588.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>57</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>11748.0</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>7798.0</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>...</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.291373</td>\n",
       "      <td>0.529026</td>\n",
       "      <td>0.806779</td>\n",
       "      <td>1.303539</td>\n",
       "      <td>1.810671</td>\n",
       "      <td>2.433295</td>\n",
       "      <td>0.332263</td>\n",
       "      <td>0.557223</td>\n",
       "      <td>1.426901</td>\n",
       "      <td>1.871972</td>\n",
       "      <td>3.256410</td>\n",
       "      <td>5.240602</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22480</th>\n",
       "      <td>25588</td>\n",
       "      <td>25589.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>20410.0</td>\n",
       "      <td>1043.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>58</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>11748.0</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>7798.0</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>...</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.136224</td>\n",
       "      <td>0.467290</td>\n",
       "      <td>0.737317</td>\n",
       "      <td>1.052907</td>\n",
       "      <td>1.617338</td>\n",
       "      <td>2.193553</td>\n",
       "      <td>0.256627</td>\n",
       "      <td>0.674157</td>\n",
       "      <td>0.956848</td>\n",
       "      <td>2.049708</td>\n",
       "      <td>2.608997</td>\n",
       "      <td>4.348718</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22481</th>\n",
       "      <td>25589</td>\n",
       "      <td>25590.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>25374.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>59</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>20410.0</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>11748.0</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>7798.0</td>\n",
       "      <td>...</td>\n",
       "      <td>289.0</td>\n",
       "      <td>0.243214</td>\n",
       "      <td>0.412570</td>\n",
       "      <td>0.824155</td>\n",
       "      <td>1.159857</td>\n",
       "      <td>1.552203</td>\n",
       "      <td>2.253911</td>\n",
       "      <td>0.318313</td>\n",
       "      <td>0.656627</td>\n",
       "      <td>1.207063</td>\n",
       "      <td>1.579737</td>\n",
       "      <td>3.020468</td>\n",
       "      <td>3.757785</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22482</th>\n",
       "      <td>25590</td>\n",
       "      <td>25591.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>28768.0</td>\n",
       "      <td>1772.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>25374.0</td>\n",
       "      <td>20410.0</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>11748.0</td>\n",
       "      <td>9942.0</td>\n",
       "      <td>...</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0.133759</td>\n",
       "      <td>0.409505</td>\n",
       "      <td>0.601514</td>\n",
       "      <td>1.068152</td>\n",
       "      <td>1.448757</td>\n",
       "      <td>1.893583</td>\n",
       "      <td>0.288727</td>\n",
       "      <td>0.698945</td>\n",
       "      <td>1.134940</td>\n",
       "      <td>1.844302</td>\n",
       "      <td>2.324578</td>\n",
       "      <td>4.181287</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22483</th>\n",
       "      <td>25591</td>\n",
       "      <td>25592.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>61</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>28768.0</td>\n",
       "      <td>25374.0</td>\n",
       "      <td>20410.0</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>11748.0</td>\n",
       "      <td>...</td>\n",
       "      <td>533.0</td>\n",
       "      <td>0.221357</td>\n",
       "      <td>0.384725</td>\n",
       "      <td>0.721509</td>\n",
       "      <td>0.956021</td>\n",
       "      <td>1.525953</td>\n",
       "      <td>1.990807</td>\n",
       "      <td>0.304176</td>\n",
       "      <td>0.680727</td>\n",
       "      <td>1.215724</td>\n",
       "      <td>1.784337</td>\n",
       "      <td>2.709470</td>\n",
       "      <td>3.335835</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22484</th>\n",
       "      <td>25592</td>\n",
       "      <td>25593.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>39885.0</td>\n",
       "      <td>2808.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>62</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>28768.0</td>\n",
       "      <td>25374.0</td>\n",
       "      <td>20410.0</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>623.0</td>\n",
       "      <td>0.135161</td>\n",
       "      <td>0.386436</td>\n",
       "      <td>0.571885</td>\n",
       "      <td>0.954189</td>\n",
       "      <td>1.220397</td>\n",
       "      <td>1.867362</td>\n",
       "      <td>0.215058</td>\n",
       "      <td>0.584650</td>\n",
       "      <td>1.042182</td>\n",
       "      <td>1.692234</td>\n",
       "      <td>2.383133</td>\n",
       "      <td>3.507223</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22485</th>\n",
       "      <td>25593</td>\n",
       "      <td>25594.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>49515.0</td>\n",
       "      <td>3647.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>63</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>39885.0</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>28768.0</td>\n",
       "      <td>25374.0</td>\n",
       "      <td>20410.0</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>...</td>\n",
       "      <td>830.0</td>\n",
       "      <td>0.241444</td>\n",
       "      <td>0.409238</td>\n",
       "      <td>0.721183</td>\n",
       "      <td>0.951407</td>\n",
       "      <td>1.426017</td>\n",
       "      <td>1.756499</td>\n",
       "      <td>0.298789</td>\n",
       "      <td>0.578105</td>\n",
       "      <td>1.058126</td>\n",
       "      <td>1.652364</td>\n",
       "      <td>2.496644</td>\n",
       "      <td>3.393976</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22486</th>\n",
       "      <td>25594</td>\n",
       "      <td>25595.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>57786.0</td>\n",
       "      <td>4365.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>49515.0</td>\n",
       "      <td>39885.0</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>28768.0</td>\n",
       "      <td>25374.0</td>\n",
       "      <td>20410.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1043.0</td>\n",
       "      <td>0.167040</td>\n",
       "      <td>0.448815</td>\n",
       "      <td>0.644638</td>\n",
       "      <td>1.008690</td>\n",
       "      <td>1.277371</td>\n",
       "      <td>1.831259</td>\n",
       "      <td>0.196874</td>\n",
       "      <td>0.554487</td>\n",
       "      <td>0.888793</td>\n",
       "      <td>1.463318</td>\n",
       "      <td>2.174545</td>\n",
       "      <td>3.185043</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22487</th>\n",
       "      <td>25595</td>\n",
       "      <td>25596.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>65719.0</td>\n",
       "      <td>5138.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>65</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>57786.0</td>\n",
       "      <td>49515.0</td>\n",
       "      <td>39885.0</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>28768.0</td>\n",
       "      <td>25374.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>0.137282</td>\n",
       "      <td>0.327254</td>\n",
       "      <td>0.647712</td>\n",
       "      <td>0.870418</td>\n",
       "      <td>1.284448</td>\n",
       "      <td>1.590013</td>\n",
       "      <td>0.177090</td>\n",
       "      <td>0.408829</td>\n",
       "      <td>0.829772</td>\n",
       "      <td>1.223280</td>\n",
       "      <td>1.899549</td>\n",
       "      <td>2.736727</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22488</th>\n",
       "      <td>25596</td>\n",
       "      <td>25597.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>73235.0</td>\n",
       "      <td>5982.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>66</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>65719.0</td>\n",
       "      <td>57786.0</td>\n",
       "      <td>49515.0</td>\n",
       "      <td>39885.0</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>28768.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1772.0</td>\n",
       "      <td>0.114366</td>\n",
       "      <td>0.267348</td>\n",
       "      <td>0.479047</td>\n",
       "      <td>0.836154</td>\n",
       "      <td>1.084329</td>\n",
       "      <td>1.545711</td>\n",
       "      <td>0.164266</td>\n",
       "      <td>0.370447</td>\n",
       "      <td>0.640252</td>\n",
       "      <td>1.130342</td>\n",
       "      <td>1.588490</td>\n",
       "      <td>2.375847</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22489</th>\n",
       "      <td>25597</td>\n",
       "      <td>25598.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>80110.0</td>\n",
       "      <td>6803.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>67</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>73235.0</td>\n",
       "      <td>65719.0</td>\n",
       "      <td>57786.0</td>\n",
       "      <td>49515.0</td>\n",
       "      <td>39885.0</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>0.093876</td>\n",
       "      <td>0.218978</td>\n",
       "      <td>0.386322</td>\n",
       "      <td>0.617894</td>\n",
       "      <td>1.008525</td>\n",
       "      <td>1.279998</td>\n",
       "      <td>0.137245</td>\n",
       "      <td>0.324056</td>\n",
       "      <td>0.558534</td>\n",
       "      <td>0.865369</td>\n",
       "      <td>1.422721</td>\n",
       "      <td>1.943747</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22490</th>\n",
       "      <td>25598</td>\n",
       "      <td>25599.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>87956.0</td>\n",
       "      <td>7716.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>68</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>80110.0</td>\n",
       "      <td>73235.0</td>\n",
       "      <td>65719.0</td>\n",
       "      <td>57786.0</td>\n",
       "      <td>49515.0</td>\n",
       "      <td>39885.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2808.0</td>\n",
       "      <td>0.097940</td>\n",
       "      <td>0.201010</td>\n",
       "      <td>0.338365</td>\n",
       "      <td>0.522099</td>\n",
       "      <td>0.776351</td>\n",
       "      <td>1.205240</td>\n",
       "      <td>0.134205</td>\n",
       "      <td>0.289870</td>\n",
       "      <td>0.501752</td>\n",
       "      <td>0.767698</td>\n",
       "      <td>1.115712</td>\n",
       "      <td>1.747863</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22491</th>\n",
       "      <td>25599</td>\n",
       "      <td>25600.0</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>95923.0</td>\n",
       "      <td>8464.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>69</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>87956.0</td>\n",
       "      <td>80110.0</td>\n",
       "      <td>73235.0</td>\n",
       "      <td>65719.0</td>\n",
       "      <td>57786.0</td>\n",
       "      <td>49515.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3647.0</td>\n",
       "      <td>0.090579</td>\n",
       "      <td>0.197391</td>\n",
       "      <td>0.309797</td>\n",
       "      <td>0.459593</td>\n",
       "      <td>0.659970</td>\n",
       "      <td>0.937251</td>\n",
       "      <td>0.096941</td>\n",
       "      <td>0.244157</td>\n",
       "      <td>0.414911</td>\n",
       "      <td>0.647334</td>\n",
       "      <td>0.939061</td>\n",
       "      <td>1.320812</td>\n",
       "      <td>46757980.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>498800.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index       ID  省份/州  国家/地区         日期    确诊病例数    死亡人数  预测ID  天数   日  \\\n",
       "22472  25580  25581.0    83    156 2020-03-12   2277.0    55.0  -1.0  50  12   \n",
       "22473  25581  25582.0    83    156 2020-03-13   5232.0   133.0  -1.0  51  13   \n",
       "22474  25582  25583.0    83    156 2020-03-14   6391.0   195.0  -1.0  52  14   \n",
       "22475  25583  25584.0    83    156 2020-03-15   7798.0   289.0  -1.0  53  15   \n",
       "22476  25584  25585.0    83    156 2020-03-16   9942.0   342.0  -1.0  54  16   \n",
       "22477  25585  25586.0    83    156 2020-03-17  11748.0   533.0  -1.0  55  17   \n",
       "22478  25586  25587.0    83    156 2020-03-18  13910.0   623.0  -1.0  56  18   \n",
       "22479  25587  25588.0    83    156 2020-03-19  17963.0   830.0  -1.0  57  19   \n",
       "22480  25588  25589.0    83    156 2020-03-20  20410.0  1043.0  -1.0  58  20   \n",
       "22481  25589  25590.0    83    156 2020-03-21  25374.0  1375.0  -1.0  59  21   \n",
       "22482  25590  25591.0    83    156 2020-03-22  28768.0  1772.0  -1.0  60  22   \n",
       "22483  25591  25592.0    83    156 2020-03-23  35136.0  2311.0  -1.0  61  23   \n",
       "22484  25592  25593.0    83    156 2020-03-24  39885.0  2808.0  -1.0  62  24   \n",
       "22485  25593  25594.0    83    156 2020-03-25  49515.0  3647.0  -1.0  63  25   \n",
       "22486  25594  25595.0    83    156 2020-03-26  57786.0  4365.0  -1.0  64  26   \n",
       "22487  25595  25596.0    83    156 2020-03-27  65719.0  5138.0  -1.0  65  27   \n",
       "22488  25596  25597.0    83    156 2020-03-28  73235.0  5982.0  -1.0  66  28   \n",
       "22489  25597  25598.0    83    156 2020-03-29  80110.0  6803.0  -1.0  67  29   \n",
       "22490  25598  25599.0    83    156 2020-03-30  87956.0  7716.0  -1.0  68  30   \n",
       "22491  25599  25600.0    83    156 2020-03-31  95923.0  8464.0  -1.0  69  31   \n",
       "\n",
       "       月     年  确诊病例数_1  确诊病例数_2  确诊病例数_3  确诊病例数_4  确诊病例数_5  确诊病例数_6  ...  \\\n",
       "22472  3  2020   2277.0   1695.0   1073.0    673.0    500.0    400.0  ...   \n",
       "22473  3  2020   2277.0   2277.0   1695.0   1073.0    673.0    500.0  ...   \n",
       "22474  3  2020   5232.0   2277.0   2277.0   1695.0   1073.0    673.0  ...   \n",
       "22475  3  2020   6391.0   5232.0   2277.0   2277.0   1695.0   1073.0  ...   \n",
       "22476  3  2020   7798.0   6391.0   5232.0   2277.0   2277.0   1695.0  ...   \n",
       "22477  3  2020   9942.0   7798.0   6391.0   5232.0   2277.0   2277.0  ...   \n",
       "22478  3  2020  11748.0   9942.0   7798.0   6391.0   5232.0   2277.0  ...   \n",
       "22479  3  2020  13910.0  11748.0   9942.0   7798.0   6391.0   5232.0  ...   \n",
       "22480  3  2020  17963.0  13910.0  11748.0   9942.0   7798.0   6391.0  ...   \n",
       "22481  3  2020  20410.0  17963.0  13910.0  11748.0   9942.0   7798.0  ...   \n",
       "22482  3  2020  25374.0  20410.0  17963.0  13910.0  11748.0   9942.0  ...   \n",
       "22483  3  2020  28768.0  25374.0  20410.0  17963.0  13910.0  11748.0  ...   \n",
       "22484  3  2020  35136.0  28768.0  25374.0  20410.0  17963.0  13910.0  ...   \n",
       "22485  3  2020  39885.0  35136.0  28768.0  25374.0  20410.0  17963.0  ...   \n",
       "22486  3  2020  49515.0  39885.0  35136.0  28768.0  25374.0  20410.0  ...   \n",
       "22487  3  2020  57786.0  49515.0  39885.0  35136.0  28768.0  25374.0  ...   \n",
       "22488  3  2020  65719.0  57786.0  49515.0  39885.0  35136.0  28768.0  ...   \n",
       "22489  3  2020  73235.0  65719.0  57786.0  49515.0  39885.0  35136.0  ...   \n",
       "22490  3  2020  80110.0  73235.0  65719.0  57786.0  49515.0  39885.0  ...   \n",
       "22491  3  2020  87956.0  80110.0  73235.0  65719.0  57786.0  49515.0  ...   \n",
       "\n",
       "       死亡人数_6  Trend_确诊病例数_1  Trend_确诊病例数_2  Trend_确诊病例数_3  Trend_确诊病例数_4  \\\n",
       "22472     5.0       0.000000       0.343363       1.122088       2.383358   \n",
       "22473    10.0       1.297760       1.297760       2.086726       3.876048   \n",
       "22474    17.0       0.221521       1.806763       1.806763       2.770501   \n",
       "22475    28.0       0.220153       0.490443       2.424682       2.424682   \n",
       "22476    35.0       0.274942       0.555625       0.900229       3.366271   \n",
       "22477    54.0       0.181654       0.506540       0.838210       1.245413   \n",
       "22478    55.0       0.184031       0.399115       0.783791       1.176498   \n",
       "22479   133.0       0.291373       0.529026       0.806779       1.303539   \n",
       "22480   195.0       0.136224       0.467290       0.737317       1.052907   \n",
       "22481   289.0       0.243214       0.412570       0.824155       1.159857   \n",
       "22482   342.0       0.133759       0.409505       0.601514       1.068152   \n",
       "22483   533.0       0.221357       0.384725       0.721509       0.956021   \n",
       "22484   623.0       0.135161       0.386436       0.571885       0.954189   \n",
       "22485   830.0       0.241444       0.409238       0.721183       0.951407   \n",
       "22486  1043.0       0.167040       0.448815       0.644638       1.008690   \n",
       "22487  1375.0       0.137282       0.327254       0.647712       0.870418   \n",
       "22488  1772.0       0.114366       0.267348       0.479047       0.836154   \n",
       "22489  2311.0       0.093876       0.218978       0.386322       0.617894   \n",
       "22490  2808.0       0.097940       0.201010       0.338365       0.522099   \n",
       "22491  3647.0       0.090579       0.197391       0.309797       0.459593   \n",
       "\n",
       "       Trend_确诊病例数_5  Trend_确诊病例数_6  Trend_死亡人数_1  Trend_死亡人数_2  Trend_死亡人数_3  \\\n",
       "22472       3.554000       4.692500      0.018519      0.571429      0.964286   \n",
       "22473       6.774146       9.464000      1.418182      1.462963      2.800000   \n",
       "22474       4.956198       8.496285      0.466165      2.545455      2.611111   \n",
       "22475       3.600590       6.267474      0.482051      1.172932      4.254545   \n",
       "22476       3.366271       4.865487      0.183391      0.753846      1.571429   \n",
       "22477       4.159420       4.159420      0.558480      0.844291      1.733333   \n",
       "22478       1.658639       5.108915      0.168856      0.821637      1.155709   \n",
       "22479       1.810671       2.433295      0.332263      0.557223      1.426901   \n",
       "22480       1.617338       2.193553      0.256627      0.674157      0.956848   \n",
       "22481       1.552203       2.253911      0.318313      0.656627      1.207063   \n",
       "22482       1.448757       1.893583      0.288727      0.698945      1.134940   \n",
       "22483       1.525953       1.990807      0.304176      0.680727      1.215724   \n",
       "22484       1.220397       1.867362      0.215058      0.584650      1.042182   \n",
       "22485       1.426017       1.756499      0.298789      0.578105      1.058126   \n",
       "22486       1.277371       1.831259      0.196874      0.554487      0.888793   \n",
       "22487       1.284448       1.590013      0.177090      0.408829      0.829772   \n",
       "22488       1.084329       1.545711      0.164266      0.370447      0.640252   \n",
       "22489       1.008525       1.279998      0.137245      0.324056      0.558534   \n",
       "22490       0.776351       1.205240      0.134205      0.289870      0.501752   \n",
       "22491       0.659970       0.937251      0.096941      0.244157      0.414911   \n",
       "\n",
       "       Trend_死亡人数_4  Trend_死亡人数_5  Trend_死亡人数_6   人口 (2020)  人口密度      国土面积  \\\n",
       "22472      2.235294      4.500000     10.000000  46757980.0  94.0  498800.0   \n",
       "22473      3.750000      6.823529     12.300000  46757980.0  94.0  498800.0   \n",
       "22474      4.571429      5.964286     10.470588  46757980.0  94.0  498800.0   \n",
       "22475      4.351852      7.257143      9.321429  46757980.0  94.0  498800.0   \n",
       "22476      5.218182      5.333333      8.771429  46757980.0  94.0  498800.0   \n",
       "22477      3.007519      8.690909      8.870370  46757980.0  94.0  498800.0   \n",
       "22478      2.194872      3.684211     10.327273  46757980.0  94.0  498800.0   \n",
       "22479      1.871972      3.256410      5.240602  46757980.0  94.0  498800.0   \n",
       "22480      2.049708      2.608997      4.348718  46757980.0  94.0  498800.0   \n",
       "22481      1.579737      3.020468      3.757785  46757980.0  94.0  498800.0   \n",
       "22482      1.844302      2.324578      4.181287  46757980.0  94.0  498800.0   \n",
       "22483      1.784337      2.709470      3.335835  46757980.0  94.0  498800.0   \n",
       "22484      1.692234      2.383133      3.507223  46757980.0  94.0  498800.0   \n",
       "22485      1.652364      2.496644      3.393976  46757980.0  94.0  498800.0   \n",
       "22486      1.463318      2.174545      3.185043  46757980.0  94.0  498800.0   \n",
       "22487      1.223280      1.899549      2.736727  46757980.0  94.0  498800.0   \n",
       "22488      1.130342      1.588490      2.375847  46757980.0  94.0  498800.0   \n",
       "22489      0.865369      1.422721      1.943747  46757980.0  94.0  498800.0   \n",
       "22490      0.767698      1.115712      1.747863  46757980.0  94.0  498800.0   \n",
       "22491      0.647334      0.939061      1.320812  46757980.0  94.0  498800.0   \n",
       "\n",
       "       中位年龄  城市人口比例  \n",
       "22472  45.0    80.0  \n",
       "22473  45.0    80.0  \n",
       "22474  45.0    80.0  \n",
       "22475  45.0    80.0  \n",
       "22476  45.0    80.0  \n",
       "22477  45.0    80.0  \n",
       "22478  45.0    80.0  \n",
       "22479  45.0    80.0  \n",
       "22480  45.0    80.0  \n",
       "22481  45.0    80.0  \n",
       "22482  45.0    80.0  \n",
       "22483  45.0    80.0  \n",
       "22484  45.0    80.0  \n",
       "22485  45.0    80.0  \n",
       "22486  45.0    80.0  \n",
       "22487  45.0    80.0  \n",
       "22488  45.0    80.0  \n",
       "22489  45.0    80.0  \n",
       "22490  45.0    80.0  \n",
       "22491  45.0    80.0  \n",
       "\n",
       "[20 rows x 41 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data['国家/地区']==country_dict['Spain']][50:70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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